Coherent Dataset Research Articles

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.

In situ Raman monitoring of studtite formation under alpha radiolysis in 18O-labeled water

Studtite [(UO2)(O2)(H2O)2]·2(H2O) is a secondary phase precipitating during the alteration of uranium-bearing materials. The mechanistic link between the formation of uranyl and peroxide bonds in the solid and the nature of the oxidizing species produced by water radiolysis remains to be elucidated. In order to improve our understanding of these mechanisms, an original experimental methodology in the presence of 18O isotopes and Raman spectroscopy has been developed. It appears that there is a direct chemical relationship between the peroxo ligands inside studtite and the peroxide entities of H2O2 molecule into the solution. The link between H2O2 formation mechanism in solution by the radiolysis of water, the nature of the radiation and the isotopy of the peroxo ligands inside studtite has been described thanks to a coherent set of experimental data. For the uranyl UO22+ ions, the characterization of its isotopy allows to specify the mechanism of oxidation at the UO2 /water interface. The isotopic configurations observed for the uranyl ion inside studtite, can be explained by assuming an oxidation mechanism of UO2 involving both a simple transfer of electrons by interaction with H2O2 and the incorporation of oxygen atoms from the solution into the fluorite structure via OH° radicals.

A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

AbstractMicrophysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

Mathematical models of coagulation—are we there yet?

BackgroundMathematical models of coagulation have been developed to mirror thrombin generation in plasma, with the aim of investigating how variation in coagulation factor levels regulates hemostasis. However, current models vary in the reactions they capture and the reaction rates used, and their validation is restricted by a lack of large coherent datasets, resulting in questioning of their utility. ObjectivesTo address this debate, we systematically assessed current models against a large dataset, using plasma coagulation factor levels from 348 individuals with normal hemostasis to identify the causes of these variations. MethodsWe compared model predictions with measured thrombin generation, quantifying and comparing the ability of each model to predict thrombin generation, the contributions of the individual reactions, and their dependence on reaction rates. ResultsWe found that no current model predicted the hemostatic response across the whole cohort and all produced thrombin generation curves that did not resemble those obtained experimentally. Our analysis has identified the key reactions that lead to differential model predictions, where experimental uncertainty leads to variability in predictions, and we determined reactions that have a high influence on measured thrombin generation, such as the contribution of factor XI. ConclusionThis systematic assessment of models of coagulation, using large dataset inputs, points to ways in which these models can be improved. A model that accurately reflects the effects of the multiple subtle variations in an individual’s hemostatic profile could be used for assessing antithrombotics or as a tool for precision medicine.

A Connector for Integrating NGSI-LD Data into Open Data Portals.

Nowadays, there are plenty of data sources generating massive amounts of information that, combined with novel data analytics frameworks, are meant to support optimisation in many application domains. Nonetheless, there are still shortcomings in terms of data discoverability, accessibility and interoperability. Open Data portals have emerged as a shift towards openness and discoverability. However, they do not impose any condition to the data itself, just stipulate how datasets have to be described. Alternatively, the NGSI-LD standard pursues harmonisation in terms of data modelling and accessibility. This paper presents a solution that bridges these two domains (i.e., Open Data portals and NGSI-LD-based data) in order to keep benefiting from the structured description of datasets offered by Open Data portals, while ensuring the interoperability provided by the NGSI-LD standard. Our solution aggregates the data into coherent datasets and generate high-quality descriptions, ensuring comprehensiveness, interoperability and accessibility. The proposed solution has been validated through a real-world implementation that exposes IoT data in NGSI-LD format through the European Data Portal (EDP). Moreover, the results from the Metadata Quality Assessment that the EDP implements, show that the datasets' descriptions generated achieve excellent ranking in terms of the Findability, Accessibility, Interoperability and Reusability (FAIR) data principles.

Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task.

Re-Exploring the Anthracycline Chemical Space for Better Anti-Cancer Compounds.

The anthracycline anti-cancer drugs are intensely used in the clinic to treat a wide variety of cancers. They generate DNA double strand breaks, but recently the induction of chromatin damage was introduced as another major determinant of anti-cancer activity. The combination of these two events results in their reported side effects. While our knowledge on the structure-activity relationship of anthracyclines has improved, many structural variations remain poorly explored. Therefore, we here report on the preparation of a diverse set of anthracyclines with variations within the sugar moiety, amine alkylation pattern, saccharide chain and aglycone. We assessed the cytotoxicity in vitro in relevant human cancer cell lines, and the capacity to induce DNA- and chromatin damage. This coherent set of data allowed us to deduce a few guidelines on anthracycline design, as well as discover novel, highly potent anthracyclines that may be better tolerated by patients.

Banatian DeathMetals: Radiocarbon Dating of Cremation Burials of the Setting Bronze Age and Dawning Iron Age

The lack of radiocarbon measurements of funerary contexts is a major shortcoming of the Late Bronze Age and Early Iron Age of the Eastern Carpathian Basin, especially in the Banat region. The present batch of samples tries to address these drawbacks, by detailing sampling strategies, employed pre-treatment and by providing a robust and coherent dataset of radiocarbon measurements. Implications of the new radiocarbon dates is discussed from a supra-regional perspective, while keeping aspects of typo-chronology, circulation of goods, and social nuances of employment of Bronze Age bronzes in the forefront. Ten burials were selected from four Banatian burial grounds according to the occurrence of metal finds in the funerary inventories. Beyond establishing a broad frame of absolute chronology for these sites of interment, the radiocarbon data provide reliable arguments for the precise attribution of metal discoveries. In addition, the data allow us to challenge some previously stated chronological assignments.

Simulated Inherent Optical Properties of Aquatic Particles using The Equivalent Algal Populations (EAP) model

Paired measurements of phytoplankton absorption and backscatter, the inherent optical properties central to the interpretation of ocean colour remote sensing data, are notoriously rare. We present a dataset of Chlorophyll a (Chl a) -specific phytoplankton absorption, scatter and backscatter for 17 different phytoplankton groups, derived from first principles using measured in vivo pigment absorption and a well-validated semi-analytical coated sphere model which simulates the full suite of biophysically consistent phytoplankton optical properties. The optical properties of each simulated phytoplankton cell are integrated over an entire size distribution and are provided at high spectral resolution. The model code is additionally included to enable user access to the complete set of wavelength-dependent, angularly resolved volume scattering functions. This optically coherent dataset of hyperspectral optical properties for a set of globally significant phytoplankton groups has potential for use in algorithm development towards the optimal exploitation of the new age of hyperspectral satellite radiometry.

Measuring the pitch control of professional football players using spatiotemporal tracking data

We study pitch control in football, using data from six complete seasons of the English Premier League. Our objective is to investigate features of pitch control in the data. We process the data to ensure consistency of the tracking and event datasets. This represents the largest coherent dataset analysed in the literature and allows the observation of consistent patterns across several seasons’ data. We demonstrate that teams playing in front of a crowd at home control on average more of the pitch than teams playing away, which reduces to in matches played behind closed doors. We observe that match by match the difference in pitch control between the teams has a weak, positive correlation with the difference in expected goals (Pearson correlation R = 0.38). As a further manifestation of home advantage we find that in games which the two teams have equal pitch control, on average the home team accumulates greater expected goals (). The concept of weighted pitch control is introduced, by assigning a weight to regions of the pitch. We demonstrate that pitch control of the penalty box of the out-of-possession team is negatively correlated with expected goals in each of the six seasons, and interpret this apparently counter-intuitive result.

The neutrino floor: a data-driven analysis

We reconsider the discovery limit of multi-ton direct detection dark matter experiments in the light of recent measurements of the coherent elastic neutrino-nucleus scattering process. Assuming the cross section to be a parameter entirely determined by data, rather than using its Standard Model prediction, we use the COHERENT CsI and LAr data sets to determine WIMP discovery limits. Being based on a data-driven approach, the results are thus free from theoretical assumptions and fall within the WIMP mass regions where XENONnT and DARWIN have best expected sensitivities. We further determine the impact of subleading nuclear form factor and weak mixing angle uncertainties effects on WIMP discovery limits. We point out that these effects, albeit small, should be taken into account. Moreover, to quantify the impact of new physics effects in the neutrino background, we revisit WIMP discovery limits assuming light vector and scalar mediators as well as neutrino magnetic moments/transitions. We stress that the presence of new interactions in the neutrino sector, in general, tend to worsen the WIMP discovery limit.

Spatial modeling to understand the dynamics of land cover MODIS satellite data and Markov Chain

The ever-increasing economic and population growth closely relates to the change in land cover. As a region grows, the land cover shifts from forests to agriculture and further to developed areas. Such a gradual change occurs slowly; however, understanding the land cover dynamics is essential to regional planning, especially in developing regions, including Indonesia. Despite its importance, studies on land cover changes in developing regions lack a coherent dataset, as many studies are based on ad-hoc approaches, resulting in embedded uncertainties. An example is the land cover classification using LANDSAT data in many parts of the developing countries lacking consistency. As an alternative, the MODIS dataset is a relatively new dataset produced by NASA, presenting a standardized land cover dataset with a 500×500 m resolution at global coverage. Such a standardized dataset gives consistency in land cover dynamics and future projections. This study presents the potential of MODIS and its use case in understanding land cover dynamics in an urbanizing watershed, i.e., the Majalaya Watershed in West Java, Indonesia. The study presents the use of Markov Chain Analysis in understanding the tendency of changes between land cover classes. This study also indicates future research directions to enrich the topic further.

Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques

AbstractWhen formulating a new solid propellant, one of the most important aspects of its performance is the burning rate's response to a change in pressure. For this reason, it is useful to be able to predict the burning rate response of a given propellant before the propellant formulation is created such that experimental trade studies are minimized or reduced in scale. While many theoretical and phenomenological models have been proposed to explain various aspects of energetic material combustion, little work has been made publicly available in the application of machine learning models to predicting solid propellant burning rates. To facilitate model creation, the material formulation and burning rate parameters for over 600 publicly available propellant formulations have been collected into a coherent data set. This work utilizes the large amount of publicly available data to inform a random forest machine learning (ML) model in the prediction of solid propellant burning rate parameters. This ML model operates over a large parameter space including ammonium perchlorate composite, plastic bonded high explosive, and double‐base propellants. The model's accuracy, adaptability, and prediction capabilities are presented and discussed. The effects of different materials on a propellant's expected burning rate are examined.

Trajetorias: a dataset of environmental, epidemiological, and economic indicators for the Brazilian Amazon

The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The dataset allows the investigation of the association between the Amazonian agrarian systems and their impacts on environmental and epidemiological changes, furthermore enhancing the possibilities for understanding, in a more integrated and consistent way, the scenarios that affect the Amazonian biome and its inhabitants.

Gravitational Wave Detection Based on Squeeze-and-excitation Shrinkage Networks and Multiple Detector Coherent SNR

Deep learning techniques have been applied to the detection of gravitational wave signals in the past few years. Most existing methods focus on the data obtained by a single detector. However, the signal-to-noise ratio (SNR) of gravitational wave signals in a single detector is pretty low, making it hard for deep neural networks to learn effective features. Therefore, how to use the observation signals obtained by multiple detectors in deep learning methods is a serious issue. We simulate binary neutron star signals from multiple detectors, including the Advanced LIGO and Virgo detectors. We calculate coherent SNR of multiple detectors using a fully coherent all-sky search method and obtain the coherent SNR data required for our proposed deep learning method. Inspired by the principle of attention network Squeeze-and-Excitation Networks (SENet) and the soft thresholding shrinkage function, we propose a novel Squeeze-and-Excitation Shrinkage (SES) module to better extract effective features. Then we use this module to establish a gravitational wave squeeze-and-excitation shrinkage network (GW-SESNet) detection model. We train and validate the performance of our model on the coherent SNR data set. Our model obtains satisfactory classification accuracy and can excellently complete the task of gravitational wave detection.

Developing a latent space to describe full-sphere head-related transfer functions

Several prior studies have used machine-learning techniques, such as Principal Component Analysis (PCA), to investigate the information content in unidirectional Head-Related Transfer Funcions (HRTFs). This paper builds upon this prior work to investigate the information content in full-sphere, free-field HRTFs. An artificial neural network is trained to reproduce the PCA coefficients of an HRTF from the combination of a direction vector and latent space vector identifying the HRTF. The training data comes from several different HRTF databases; the methods used to combine these databases into a single coherent dataset are described. Hyperparameter optimization techniques are then used to minimize the dimensionality of the latent space vector, providing new information about the information content required to accruately describe a full-sphere HRTF.

Digital Extended Specimens: Enabling an Extensible Network of Biodiversity Data Records as Integrated Digital Objects on the Internet.

The early twenty-first century has witnessed massive expansions in availability and accessibility of digital data in virtually all domains of the biodiversity sciences. Led by an array of asynchronous digitization activities spanning ecological, environmental, climatological, and biological collections data, these initiatives have resulted in a plethora of mostly disconnected and siloed data, leaving to researchers the tedious and time-consuming manual task of finding and connecting them in usable ways, integrating them into coherent data sets, and making them interoperable. The focus to date has been on elevating analog and physical records to digital replicas in local databases prior to elevating them to ever-growing aggregations of essentially disconnected discipline-specific information. In the present article, we propose a new interconnected network of digital objects on the Internet—the Digital Extended Specimen (DES) network—that transcends existing aggregator technology, augments the DES with third-party data through machine algorithms, and provides a platform for more efficient research and robust interdisciplinary discovery.

A Reference Section Through Fast‐Spread Lower Oceanic Crust, Wadi Gideah, Samail Ophiolite (Sultanate of Oman): Whole Rock Geochemistry

AbstractDue to its inaccessibility, no complete and coherent data set exists for the composition of modern fast‐spreading oceanic crust. We sampled outcrops through 6,500 m of fossil oceanic crust in the Oman Ophiolite (Wadi Gideah Transect) that is regarded as best analogue of fast‐spreading crust on land. Here we report a complete set of whole‐rock major and trace element data displaying systematic and contrasting compositional trends in lower and upper gabbros being correlated with stratigraphic depth. A significant discontinuity in crystallization regime is observed at ∼3,525 m above the mantle‐crust boundary: gabbros below ∼3,525 m have in general very low incompatible element mass fractions which develop upwards in a barely noticeable way to more differentiated compositions while Mg# decreases. More pronounced trends indicating progressive fractionation of ascending melts can be observed for incompatible elements and their element ratios as a consequence of in situ crystallization. Locally, more variable compositions within narrow depth intervals testify for advanced differentiation in situ within individual sills. Gabbros above ∼3,525 m become significantly more evolved and show considerable variations in composition. Fractional crystallization and mixing processes in a transient axial melt lens control the composition of isotropic “varitextured” gabbros and sheeted dike basalts where fractionation of high field strength elemental ratios is minor. New average compositions of fast‐spread (paleo) oceanic crust are reported for major and 38 trace elements. Comparison with new data from Wadi Khafifah close to Wadi Gideah suggests robustness of crustal accretion processes in both space and time.

Fluid Production Dataset for the Assessment of the Anthropogenic Subsidence in the Po Plain Area (Northern Italy)

Fluid produced/injected volumes from/into underground natural formations and their spatial allocation play a key role in addressing the superposition of anthropogenic subsidence effects, but the definition of coherent datasets is usually very challenging. In this paper, the creation of a gas and water production dataset for the Po Plain area in northern Italy is presented, focusing on the Emilia-Romagna region (an industrialized, highly-populated area characterized by rapid subsidence). The produced volumes and their spatial/temporal allocation are gathered from different sources, analyzed, and organized via dedicated georeferenced maps. The geological framework of the Po Plain is delineated, with attention to the superficial aquifers. Reference ranges of petrophysical and pseudo-elastic parameters are reported for both aquifer and reservoir formations. Water extractions from the superficial unconsolidated sediments are widespread, both in space and time; instead, primary gas production and underground storage of natural gas, involving deeper formations, are spatially and temporally well constrained. Drastic increases in water production and high concentrations of gas production temporally coincided between the 1950s and 1970s. The ‘hotspots’ of the strongest superposition are recognized in Piacenza, Ferrara, Bologna, and Ravenna provinces. Qualitative and quantitative information represent a reference source for both Oil and Gas Societies and Regional/National authorities in addressing the subsidence analysis to plan the field production life and predict the environmental consequences.

The “Coherent Data Set”: Combining Patient Data and Imaging in a Comprehensive, Synthetic Health Record

The “Coherent Data Set” is a novel synthetic data set that leverages structured data from Synthea™ to create a longitudinal, “coherent” patient-level electronic health record (EHR). Comprised of synthetic patients, the Coherent Data Set is publicly available, reproducible using Synthea™, and free of the privacy risks that arise from using real patient data. The Coherent Data Set provides complex and representative health records that can be leveraged by health IT professionals without the risks associated with de-identified patient data. It includes familial genomes that were created through a simulation of the genetic reproduction process; magnetic resonance imaging (MRI) DICOM files created with a voxel-based computational model; clinical notes in the style of traditional subjective, objective, assessment, and plan notes; and physiological data that leverage existing System Biology Markup Language (SBML) models to capture non-linear changes in patient health metrics. HL7 Fast Healthcare Interoperability Resources (FHIR®) links the data together. The models can generate clinically logical health data, but ensuring clinical validity remains a challenge without comparable data to substantiate results. We believe this data set is the first of its kind and a novel contribution to practical health interoperability efforts.