Use Of Datasets Research Articles

Resolving Conflicts in Data Through Curation-informed Weight Distribution Networks

Missing and conflicting data values create problems when integrating datasets from multiple collections. Moreover, when the collections to be integrated are large and continuously updated, it is not feasible to manually resolve these problems. Instead, disagreements and gaps should be resolved in an automated fashion. To achieve good quality integrated datasets automatically we introduce the Curation-informed Weight Distribution Network (CiWDN), a method that suggests which collection is more reliable in providing a data value in question. CiWDN adapts the PageRank algorithm (PR) to assign and distribute weights across data fields present in the different collections. Weight assignment is rooted in data curation best practices as metrics of a collection's reliability. The metrics include: a) data completeness, b) data coincidence, and c) data consistency over time. Final weights used as collection ranks provide the basis to resolve conflicts between different collections contributing a data value for a given field. CiWDN relies on a data dictionary that normalizes fields across collections, and is implemented on a graph database. We demonstrate CiWDN’s capability using the case of ASTRIAGraph, a knowledge system built to increase transparency of activities in Earth’s orbital environment. CiWDN can assess the reliability of data collections that conflict on space object characteristic data fields, which can be used to resolve the differences. This method for computing collections' reliability can be ported to curate other types of large integrated datasets for use in machine learning and other data-driven applications.

A practitioner-oriented regional hydrology data product for use in site-specific hydraulic applications

In the 465,000 km2 Canadian Prairies ecozone, robust hydrological input data for hydraulic model applications are uncommon because of the sparse monitoring network and the intermittently connected stream network. New hydrological datasets can offer a valuable advancement for making water management decisions and designing infrastructure in this water stressed region. The Prairie Hydrology Design and Analysis Product (PHyDAP) was created to address existing limitations, and provides a comprehensive regional dataset for use in hydraulic modelling applications. PHyDAP is a collection of outputs from a physically based hydrological modelling framework, run for periods ranging from 38 to 150 years, according to three climate forcing datasets. The dataset includes vertical and lateral fluxes (rainfall, snowmelt, upland runoff, and open water evaporation) and basin streamflow, at hourly or 3-hourly intervals for the >4000 small basins of approximately 100 km2 that span the region. This contribution describes the motivation for this work and methodology used to derive the data product, summarizes the data and its accessibility, and provides an overview of potential use cases.

A global flash drought inventory based on soil moisture volatility

Flash droughts, characterized by rapid onset and development, present significant challenges to agriculture and climate mitigation strategies. Operational drought monitoring systems, based on precipitation, soil moisture deficits, or temperature anomalies, often fall short in timely detection of these events, underscoring the need for customized identification and monitoring indices that account for the rapidity of flash drought onset. Recognizing this need, this paper introduces a global flash drought inventory from 1990 to 2021 derived using the Soil Moisture Volatility Index (SMVI). Our work expands the application of the SMVI methodology, previously focused on the United States, to a global scale, providing a tool for understanding and predicting these rapidly developing phenomena. The dataset encompasses detailed event characteristics, including onset, duration, and severity, across diverse climate zones. By integrating atmospheric variables through their impact on soil moisture, the inventory offers a platform for analyzing the drivers and impacts of flash droughts, and serves as a large, consistent dataset for use in training and evaluating flash drought prediction models.

Open-Source Optimization of Hybrid Monte Carlo Methods for Fast Response Modeling of NaI (Tl) and HPGe Gamma Detectors

Modeling the response of gamma detectors has long been a challenge within the nuclear community. Significant research has been conducted to digitally replicate instruments that can cost over USD 100,000 and are difficult to operate outside of a laboratory setting. The large cost and availability prevent some from making use of such equipment. Subsequently, there have been multiple attempts to create cost-effective codes that replicate the response of sodium-iodide and high-purity germanium detectors for data derivation related to gamma-ray interaction with matter. While robust programs do exist, they are often subject to export controls and/or they are not intuitive to use. Through the use of hybrid Monte Carlo methods, MATLAB can be used to produce a fast first-order response of various gamma-ray detectors. The combination of a graphical user interface with a numerical-based script allows for open-source and intuitive code. When benchmarked with experimental data from Co-60, Cs-137, and Na-22, the code can numerically calculate a response comparable to experimental and industry-standard response codes. Evidence supports both savings in computational requirements and the inclusion of an intuitive user experience that does not heavily compromise data when compared to other standard codes, such as MCNP and GADRAS, or experimental results. When the application is installed on a Dell Intel i7 computer with 16 cores, the average time to simulate the benchmarked isotopes is 0.26 s. Installation on an HP Intel i7 four-core machine runs the same isotopes in 1.63 s. The results indicate that simple gamma detectors can be modeled in an open-source format. The anticipation for the MATLAB application is to be a tool that can be easily accessible and provide datasets for use in an academic setting requiring gamma-ray detectors. Ultimately, this article provides evidence that hybrid Monte Carlo codes in an open-source format can benefit the nuclear community in both computational time and up-front cost for access.

A Generalized Area-Biased Power Ishita Distribution - Properties and Applications

Objectives: Generalized Area-Biased Power Ishita Distribution (GAPID) is a brand-new distribution that was suggested in this study. Its characterization is also mentioned in detail. Methods: The idea of weighted distributions is incorporated. This helps to derive new distributions that will be a best fit for many real-life data from many domains like agriculture, biomedical, financial, etc., where they may not match with the conventional distributions. Thus, it becomes necessary to create new distributions. The parameters have been calculated by maximum likelihood estimation. Findings: Its hazard rate function, survival function, and Moments, among other statistical features, were explored. Novelty: As results from classical distributions are insufficient for many Biomedical datasets, this novel distribution was fitted to a real data set of lung cancer patients' survival periods in months, allowing for a discussion of the data set's use. The superiority of the distribution is tested by comparing the same with known distributions and finding this one is better. Thereby the importance of the said distribution is established. Keywords: Estimate, Weighted distributions, Parameters, Reliability, Ishita distribution

Recommendations for data collection in cohort studies of preterm born individuals - The RECAP Preterm Core Dataset.

Preterm birth (before 37 completed weeks of gestation) is associated with an increased risk of adverse health and developmental outcomes relative to birth at term. Existing guidelines for data collection in cohort studies of individuals born preterm are either limited in scope, have not been developed using formal consensus methodology, or did not involve a range of stakeholders in their development. Recommendations meeting these criteria would facilitate data pooling and harmonisation across studies. To develop a Core Dataset for use in longitudinal cohort studies of individuals born preterm. This work was carried out as part of the RECAP Preterm project. A systematic review of variables included in existing core outcome sets was combined with a scoping exercise conducted with experts on preterm birth. The results were used to generate a draft core dataset. A modified Delphi process was implemented using two stages with three rounds each. Three stakeholder groups participated: RECAP Preterm project partners; external experts in the field; people with lived experience of preterm birth. The Delphi used a 9-point Likert scale. Higher values indicated greater importance for inclusion. Participants also suggested additional variables they considered important for inclusion which were voted on in later rounds. An initial list of 140 data items was generated. Ninety-six participants across 22 countries participated in the Delphi, of which 29% were individuals with lived experience of preterm birth. Consensus was reached on 160 data items covering Antenatal and Birth Information, Neonatal Care, Mortality, Administrative Information, Organisational Level Information, Socio-economic and Demographic information, Physical Health, Education and Learning, Neurodevelopmental Outcomes, Social, Lifestyle and Leisure, Healthcare Utilisation and Quality of Life. This core dataset includes 160 data items covering antenatal care through outcomes in adulthood. Its use will guide data collection in new studies and facilitate pooling and harmonisation of existing data internationally.

Effects of hydraulic conductivity on simulating groundwater–land surface interactions over a typical endorheic river basin

Hydraulic conductivity is the key variable in subsurface configuration that is important in modeling groundwater and its interactions with land surface processes. However, an acquisition of quantitatively distributed hydraulic conductivity is challenging due to limitations on data availability, accuracy and resolution, which inhibits an advanced understanding of hydrologic and energy cycles at the basin scale. This study investigated the role of hydraulic conductivity in simulating groundwater-land surface interactions over a typical endorheic river basin in northwestern China. A 3D variably saturated groundwater model coupled to a land surface model was configured to account for interactions between groundwater dynamics and land energy fluxes. Four scenarios of hydraulic conductivity were tested and inter-compared, adopting both homogeneous and heterogeneous parameterization strategies based on Global Soil Dataset for use in Earth System Models (GSDE) and HYdrogeology MaPS (GLHYMPS). The overall objectives are to diagnose effects of hydraulic conductivity on simulating groundwater–land surface interactions, with a particular focus on differences in spatial distributions and statistical characteristics between scenarios, as well as the dynamics and feedback mechanisms between groundwater and land surface energy fluxes. Comprehensive simulations and analyses were conducted to discern spatial distributions and statistical characteristics of key hydrological variables (streamflow, water table depth, latent heat flux, sensible heat flux) under different scenarios. Results showed that the hydraulic conductivity of the saturated zone had a significant control over water table depth, while the combine hydraulic conductivity in the vadose zone and the saturated zone governed the evolution of streamflow. Moreover, the water table critical range—where the land energy budget is highly sensitive to groundwater table—was 0.1 m–5 m and the area might expand approximately 4.5 % after considering the heterogeneity of hydraulic conductivity. Compared to the homogeneous scenario, the spatial heterogeneity of hydraulic conductivity amplified water table depth variability while diminishing land energy fluxes variability.

Every Dataset is a Canon

AbstractFor architects, the construction of a dataset for use in AI, whether imported from somewhere else or newly created, is fundamentally an epistemological canon of references, likes and preoccupations often predicated on the selection of so‐called masterpieces of Western art. The canon represents ideals of beauty and knowledge, but it also reflects a bias in favour of art created by those who have occupied the most socially, politically and economically powerful positions in culture. Mario Carpo, architectural historian, critic and Reyner Banham Professor of Architectural History and Theory at the Bartlett School of Architecture, University College London, leads us through a succinct series of eras and architectural styles to demonstrate this fact and their lack of diversity.

Screening Publicly Available CT Datasets of the Head for Potential Use in Studying Normal and Pathological Issues Related to Nasal Airflow

Objective: The purpose of this study was two-fold. First, we wanted to develop optimized strategies for evaluating publicly available CT datasets for use in simulated studies of airflow. Second, we sought to generate 3D models of the nasal cavity from these CT datasets and perform Computation Fluid Dynamics (CFD) modeling of airflow in the generated models. This work lays the groundwork for future investigations into studying diversity in airflow patterns and rates, analyzing microparticle and odorant deposition, how anatomical variations relate to difference in airflow and pathologies, and the role of comparative CFD analyses in surgical planning. Methods: 490 CT scans were downloaded from the CQ500 repository dataset and evaluated for effectiveness in building digital models of the nasal airway. Scans were digitally dissected and analyzed in 3D Slicer to determine eligibility for model creation and simulation. Eligible scans were further edited in 3D Slicer. All ethmoid air cells, sinuses and recesses were removed, as it was determined these areas do not contribute to the airflow we want to study. These reconstructed models made in 3D Slicer were further refined using the program MeshMixer. Once editing was completed in MeshMixer, STL files were imported back into 3D Slicer to be overlayed with the original STL file created directly from the CT scan to verify anatomical accuracy. Any inaccuracies were removed with further editing in MeshMixer. Models were then imported into Salome, where they were converted into “solid models” for CFD analysis. All CFD was performed in OpenFOAM, and results were visualized using ParaView. Results: Five models have been reconstructed and are being subjected to CFD analysis. To be a strong candidate for modeling, the CT scan had to include at least 230 slices, show all anatomy relevant to the study including turbinates, ethmoid air cells, the cribriform plate and olfactory region, nasal vestibules, and the nasopharynx, and display adequate detail. Of the five final models, four were deemed anatomically normal and all relevant anatomy was intact. One of the five models displayed a deviated septum. The four “normal variants” were used to predict normal patterns of airflow using Computational Fluid Dynamics (CFD). The results of these models compared with the deviated septum model show severe limitation of airflow in the deviated model, together with compensatory flow on the more open side of the airway. Relatively simple digital "corrections" were applied, which effectively recovered normal airflow. Conclusion: CT scans from the publicly available CQ500 repository dataset are effective in creating digital models of the nasal airway to be used for CFD. Computational modeling was shown to be a useful tool in understanding how nasal morphology contributed to the diversity in airflow patterns and rates, microparticle and odorant deposition. Thus far 5 CT scans out of 490 specimens were eligible for modeling; however, due to time constraints of the study, these were the only scans that were utilized. Four of these models were “normal variants” while the scan with a deviated septum was a “pathological variant.” One of the normal models is currently being run in the OpenFOAM CFD program and shows promising results with similar airflow patterns through both the left and right airways. All other models will also be analyzed in this CFD program and more rounds of CFD will be run to compare airflow dynamics for normal and pathological variants. This work provides insights into how a broad, comparative sample of subjects can be used in surgical planning and in understanding the physiological consequences of potential surgical interventions. Burrell College. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract 1750: Decoding breast cancer: Unraveling subtype and model differences through multi-model single-cell RNA sequencing data integration

Abstract Breast cancer's complex transcriptional landscape requires a deep understanding of sample and cell diversity to identify effective treatments. In this study, we amalgamate single-cell RNA sequencing data from breast cancer patient-derived xenografts (PDX), organoids, cell lines, patient tumors and reduction mammoplasties resulting in a comprehensive dataset of 117 samples with 506,719 total cells. These samples encompass hormone receptor positive (HR+), human epidermal growth factor receptor 2 enriched (HER2E), and triple-negative breast cancer (TNBC) subtypes. We aimed to delineate similarities and distinctions across model systems and patient samples while also exploring stratification of therapeutic drug efficacy based on subtype proportions within tumors. Mammary tumor PDXs, organoids, and established cell lines exhibited higher proliferation and lower heterogeneity observed via UMAP dimensionality reduction compared to most patient tumors or normal breast epithelium. TNBCs had elevated proliferative and pro-metastatic signatures compared to HR+ and HER2E samples. Interestingly, compared to matched PDX tumors, organoids from these same models were found to exhibit stark differences in gene expression, including upregulation of metabolically active aldo-keto reductase family genes, highlighting differences in the model systems with implications for pre-clinical drug testing. Single-cell tumor subtyping analyses with scSubtype and TNBCtype methods found that therapeutically treated samples had shifts in the proportions of cell-wise subtype annotations when compared to matched untreated samples. Similarly, patient lymph node metastasis when compared with matched primary tumors were significantly linked to decreases in Basal-like and HER2-enriched cell-wise annotations in untreated ER+ samples. In vitro assessment of anti-cancer compounds on PDX cells showed significant correlation of subtype proportion with cell viability following treatment with targeted therapeutic agents. This subtyping methodology offers a powerful tool to monitor the evolving gene expression landscape within samples and predict responses to therapeutic agents. We present here a dynamic approach to cell-wise sample annotation and a substantial multi-model dataset for use facilitating informed decision-making in preclinical research and therapeutic development. Citation Format: Julia E. Altman, Carson J. Walker, Emily K. Zboril, Nicole S. Hairr, Rachel K. Myrick, David C. Boyd, Jennifer E. Koblenski, Madhavi Puchalapalli, Bin Hu, Mikhail G. Dozmorov, Xi Chen, Yunshun Chen, Charles M. Perou, Brian D. Lehmann, Jane E. Visvader, Amy L. Olex, J. Chuck Harrell. Decoding breast cancer: Unraveling subtype and model differences through multi-model single-cell RNA sequencing data integration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1750.

The importance of digital elevation model accuracy in XCO2 retrievals: improving the Orbiting Carbon Observatory 2 Atmospheric Carbon Observations from Space version 11 retrieval product

Abstract. Knowledge of surface pressure is essential for calculating column-averaged dry-air mole fractions of trace gases, such as CO2 (XCO2). In the NASA Orbiting Carbon Observatory 2 (OCO-2) Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, the retrieved surface pressures have been found to have unacceptable errors, warranting a parametric bias correction. This correction depends on the difference between retrieved and a priori surface pressures, which are derived from a meteorological model that is hypsometrically adjusted to the surface elevation using a digital elevation model (DEM). As a result, the effectiveness of the OCO-2 bias correction is contingent upon the accuracy of the referenced DEM. Here, we investigate several different DEM datasets for use in the OCO-2 ACOS retrieval algorithm: the OCODEM used in ACOS v10 and previous versions, the NASADEM+ (a composite of SRTMv4, ASTER GDEMv3, GIMP, and RAMPv2 DEMs) used in ACOS v11, the Copernicus GLO-90 DEM (GLO-90 DEM), and two polar regional DEMs (ArcticDEM and REMA). We find that the NASADEM+ (ASTER GDEMv3) has a persistent negative bias on the order of 10 to 20 m across most regions north of 60° N latitude, relative to all the other DEMs considered (OCODEM, ArcticDEM, and GLO-90 DEM). Variations of 10 m in DEM elevations lead to variations in XCO2 of approximately 0.4 ppm, meaning that the XCO2 from OCO-2 ACOS v11 retrievals tends to be 0.4 to 0.8 ppm lower across regions north of 60° N than XCO2 from OCO-2 ACOS v10. Our analysis also suggests that the GLO-90 DEM has superior global continuity and accuracy compared to the other DEMs, motivating a post-processing update from OCO-2 v11 Lite files (which used NASADEM+) to OCO-2 v11.1 by substituting the GLO-90 DEM globally. We find that OCO-2 v11.1 improves accuracy and spatial continuity in the bias-corrected XCO2 product relative to both v10 and v11 in high-latitude regions while resulting in marginal or no change in most regions within ± 60° latitude. In addition, OCO-2 v11.1 provides increased data throughput after quality control filtering in most regions, partly due to the change in DEM but mostly due to other corrections to quality control parameters. Given large-scale differences north of 60° N between the OCODEM and NASADEM+, we find that replacing the OCODEM with NASADEM+ yields a ∼ 100 TgC shift in inferred carbon uptake for the zones spanning 30 to 60° N and 60 to 90° N, which is on the order of 5 % to 7 % of the estimated pan-Arctic land sink. Changes in inferred fluxes from replacing the OCODEM with the GLO-90 DEM are smaller, and given the evidence for improved accuracies from this DEM, this suggests that large changes in inferred fluxes from the NASADEM+ are likely erroneous.

Combining IC50 or Ki Values from Different Sources Is a Source of Significant Noise.

As part of the ongoing quest to find or construct large data sets for use in validating new machine learning (ML) approaches for bioactivity prediction, it has become distressingly common for researchers to combine literature IC50 data generated using different assays into a single data set. It is well-known that there are many situations where this is a scientifically risky thing to do, even when the assays are against exactly the same target, but the risks of assays being incompatible are even higher when pulling data from large collections of literature data like ChEMBL. Here, we estimate the amount of noise present in combined data sets using cases where measurements for the same compound are reported in multiple assays against the same target. This approach shows that IC50 assays selected using minimal curation settings have poor agreement with each other: almost 65% of the points differ by more than 0.3 log units, 27% differ by more than one log unit, and the correlation between the assays, as measured by Kendall's τ, is only 0.51. Requiring that most of the assay metadata in ChEMBL matches ("maximal curation") in order to combine two assays improves the situation (48% of the points differ by more than 0.3 log units, 13% by more than one log unit, and Kendall's τ is 0.71) at the expense of having smaller data sets. Surprisingly, our analysis shows similar amounts of noise when combining data from different literature Ki assays. We suggest that good scientific practice requires careful curation when combining data sets from different assays and hope that our maximal curation strategy will help to improve the quality of the data that are being used to build and validate ML models for bioactivity prediction. To help achieve this, the code and ChEMBL queries that we used for the maximal curation approach are available as open-source software in our GitHub repository, https://github.com/rinikerlab/overlapping_assays.

Recommended electron-impact excitation and ionization cross sections for Be II

An overview of the current status of electron-impact excitation and ionization cross sections for Be II is given and the recommended data sets for use in plasma modeling are presented. Accurate cross sections between the lowest 14 atomic terms of 1s2nl(n≤5) configurations are calculated with the convergent close-coupling (CCC) method and compared with the available experimental and theoretical results. The recommended electron-impact excitation and ionization cross sections are represented by analytical fitting functions that preserve correct asymptotic behavior. Uncertainties in the recommended data sets are determined by assessing the accuracy of the target structures, the convergence in the subsequent collisional calculations, and the fitting method. They are well within 15 % for most of the transitions. The fitting coefficients for 91 excitation cross sections and 14 ionization cross sections are provided for use in plasma modeling simulations.

Containerizing neuroimaging workflows for scalable and reproducible analyses

AbstractBackgroundLarge multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.MethodWe demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.ResultContainerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.ConclusionContainerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.

Containerizing neuroimaging workflows for scalable and reproducible analyses

AbstractBackgroundLarge multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.MethodWe demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.ResultContainerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.ConclusionContainerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.

Comparing the suitability of global gridded population datasets for local landslide risk assessments

AbstractLandslide risk assessments are increasingly crucial for meeting global disaster risk reduction strategies and mobilising knowledge for local governments to protect communities and infrastructure. These assessments are data intensive, requiring large amounts of spatial demographic and natural hazard information. There are a growing number of high-resolution gridded population datasets that have global coverage with significant potential to transform disaster risk modelling, however landslide research has not yet compared the suitability of these gridded datasets for local risk assessments. Combining social vulnerability indicators with high-resolution settlement layer (HRSL) and WorldPop gridded population datasets, as well as a local household survey, we layer landslide susceptibility maps to compare three landslide risk estimates, examining the case of the Municipality of Carigara located in the central Philippines. Using statistical t tests, we compare aggregated community landslide risk for 49 communities. Findings revealed that HRSL data resulted in similar landslide risk at community scales when compared to local surveys, however WorldPop data greatly overestimated risk. Our findings point to a high level of accuracy of HRSL when used as an exposure dataset for local landslide risk studies and recommend avoiding WorldPop for such purposes. This research advances understanding of the suitability of open population datasets for use in landslide risk assessments in resource-constrained communities.

A Method to Screen, Assess, and Prepare Open Data for Use

Open data's value-creating capabilities and innovation potential are widely recognized, resulting in a notable increase in the number of published open data sources. A crucial challenge for companies intending to leverage open data is to identify suitable open datasets that support specific business scenarios and prepare these datasets for use. Researchers have developed several open data assessment techniques, but those are restricted in scope, do not consider the use context, and are not embedded in the complete set of activities required for open data consumption in enterprises. Therefore, our research aims to develop prescriptive knowledge in the form of a meaningful method to screen, assess, and prepare open data for use in an enterprise setting. Our findings complement existing open data assessment techniques by providing methodological guidance to prepare open data of uncertain quality for use in a value-adding and demand-oriented manner, enabled by knowledge graphs and linked data concepts. From an academic perspective, our research conceptualizes open data preparation as a purposeful and value-creating process.

Curated Datasets for Use in Automated Media Monitoring and Feedback System: “News Classification System” Dataset, “Government News Classification” Dataset

Online journalism in India, a growing field that involves news websites and Digital media, connects with the Press Information Bureau (PIB), a government agency dedicated to sharing accurate information about government policies and initiatives with journalists. While various news outlets publish diverse articles and opinions on these topics, the government seeks to leverage Artificial Intelligence and Machine Learning for gathering feedback in multiple languages. To develop such a system, a notable obstacle is the lack of a readily accessible standard dataset is required. To address this, two datasets are developed named, 'NCS' and 'GNC,' consisting of information from 2020 to 2023 and collected through web scraping tools like Parsehub and manually scrapping. NCS represents News Classification system dataset and GNC represents Government News Classification. The 'NCS' dataset includes Indian news in Hindi, Marathi, and English with categorization of Indian news as government-related or not. Then, a Machine Learning model called "Government News Classifier" to sort news articles using the 'NCS' dataset into either government-related or non-government-related categories. The objective is to use this model to figure out if a news source is discussing topics related to the government or not. Using this model, we created the 'GNC' dataset, which contains only news articles related to government schemes and policies in Hindi, Marathi, and English. In GNC dataset, Human experts manually classify each news source into three categories: "government favourable," "government non-favourable," or "neutral." In essence, this research emphasizes the importance of having access to a large dataset, which can stimulate more advanced prediction models in this complex field.

Continental Scale Hydrostratigraphy: Basin-Scale Testing of Alternative Data-Driven Approaches.

Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data-driven approaches for estimating hydraulic conductivity and subsurface properties at the continental-scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large-scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large-and continental-scale hydrologic modeling.

Development of seismic fragilities for a base station steel lattice cellular tower

A large portion of the country of Turkey is located in a very high seismic region known as a first-degree earthquake zone where earthquakes occur frequently. Earthquakes result in damage to infrastructure including base station towers and subsequently failure of mobile communication networks. The loss of functionality of mobile communication networks following an earthquake has a direct impact on emergency response and both short and long-term recovery phases. Understanding both these phases requires systematic modeling of infrastructure components, which rely on fragility curves representing the probabilistic relationship between seismic intensity and damage. This study focuses on the development of damage fragility curves (fragilities) for an archetypical mobile cellular tower, namely a 55 m tall steel lattice tower (SLT) used in base station towers in Turkey. Nonlinear time history analysis was performed using a robust suite of far field ground motion records to model the performance of the archetype tower and develop damage fragilities for five specific damage states which were defined in terms of likely performance: damage state 0 (no damage), damage state 1 (uninterrupted communication), damage state 2 (communication interruption), damage state 3 (communication failure), damage state 4 (structural failure). While fragility curve has been developed for lattice towers of different types and sizes in the literature, there is no specific study for 55 m SLT and the novelty of this study is that the resulting SLT archetype fragilities can serve as a benchmark dataset for use in community-level risk and resilience analyses to improve response and recovery planning following earthquakes.