Use Of Datasets Research Articles

The First Complete Mitochondrial Genome Sequence in the Genus Aphanius (Teleostei)

In the present study, for the first time we isolated and characterized the complete mitochondrial genome sequence of the endangered Farsi killifish Aphanius farsicus by long polymerase chain reaction amplification, and primer walking methods. The circular mitogenome of A. farsicus consisting of 16 485 base pairs encodes 13 polypeptides (protein-coding genes), the 12S and 16S ribosomal RNAs, and 22 transfer RNAs and an 884 bp D-loop control region. These genes are ordered in the same way as most other vertebrates. The overall nucleotide composition of this genome was 27.09 for A; 27.87 for T; 16.89 for G; and 28.14% for C (GC content of 45%, and AT content of 55%). The genus Aphanius has already been in the family Cyprinodontidae. However, the name Aphaniidae has recently been proposed as valid family for the members of the genus Aphanius (the Western Palaearctic killifishes), while the family Cyprinodontidae is restricted to the New World genera such as Cyprinodon, Floridichthys and Jordanella. Based on the phylogenetic relationships achieved in the present study, we recommend that the validation of family Aphaniidae still needs more phylogenetic supports, and this can be investigated by adding more sequences of the Aphanius species. The availability of this mitogenome will also provide a set of useful data for studying on population genetic diversity and molecular evolution and facilitate evaluations of A. farsicus genetic structure for management and conservation of this endangered species.

Comparing Historical Patent Datasets

I provide a primer on six recent large-scale historical patent datasets for use in innovation research. I discuss how each dataset is constructed, the types of patent information included in each, and the quality and completeness of each. Throughout, I emphasize when our knowledge of the history of invention is dependent on the data source used and provide recommendations about which dataset is most likely to be best for different contexts. Overall, these datasets paint a remarkably consistent picture of the history of U.S. invention. When the datasets do disagree, these differences tend to be minor, although I highlight some important exceptions. I further describe several niche historical patent datasets that allow researchers to study institutional contexts that cannot be studied using modern patent data. Finally, I discuss features of patent data that are not available for the historical patents but are available for modern patents.

The complete mitochondrial genome of Pareuchiloglanis myzostoma (Teleostei, Siluriformes)

Pareuchiloglanis myzostoma is a key-listed protected indigenous fish species in Nujiang, Yunnan, China. In this study, we firstly reported the complete mitochondrial genome of P. myzostoma, which was 16,584 bp in length, containing 13 protein-coding genes, 22 transfer RNAs (tRNAs), 2 ribosomal RNA genes (rRNAs), and a non-coding control region (D-loop). The overall base composition of P. myzostoma was 30.7% for A, 24.2% for T, 16.0% for G, and 29.1% for C. Phylogenetic analysis showed that all Sisoridae species clustered together formed a monophyletic group. This work would provide a set of useful data on further molecular evolution studies of this precious species.

Developing a spatially and temporally explicit solar resource dataset for Great Britain

Existing dispatch and infrastructure investment tools for modelling the Scottish energy system do not currently include solar photovoltaic resource. This study describes the development of three spatially and temporally explicit solar resource datasets for use in these models, for domestic, commercial roof-mounted and ground-mounted arrays. They include the whole of Great Britain (GB) and are based on empirical weather data for 2000–2015. They are expected to be a valuable resource for energy system modelling in GB.

EVM-CNN: Real-Time Contactless Heart Rate Estimation From Facial Video

With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated the heart rate (HR) from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, like the processing time increasing with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that HR information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this paper to remotely estimate the HR under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach shows better performance compared with the benchmark on the MMSE-HR dataset in terms of both the average HR estimation and short-time HR estimation. High consistency in short-time HR estimation is observed between our method and the ground truth.

Abstract 1690: A tumor deconvolution DREAM Challenge: Inferring immune infiltration from bulk gene expression data

Abstract Introduction: Immune cell infiltration in solid tumors correlates with patient outcome and therapeutic response. While specific cell-type infiltration can be elucidated by single-cell transcriptomic techniques, these suffer biases and limitations of scale. To instead leverage existing large repositories of bulk gene expression data with clinical outcomes, computational methods have been developed to deconvolve sample profiles into their stromal (including immune) and malignant cell components. However, their performance has yet to be compared within an unbiased, objective framework. To assess methods and catalyze development of new approaches, we are organizing a community-wide DREAM Challenge. Methods: The Challenge consists of: (1) an open phase, during which methods are trained on publicly-available transcriptomic profiles of cell populations; (2) a leaderboard phase, during which methods are submitted, assessed, and revised using bulk expression data having ground truth (e.g., ratios from mixing experiments or from flow cytometry); and (3) a validation phase, during which final submissions are assessed using independent expression profiles of known admixtures. The latter are generated in vitro by mixing RNA from multiple types of purified stromal and cancer cells. To assess sensitivity, we provide in silico admixtures from expression profiles of purified populations with a range of tumor “contamination.” Models will be submitted as Docker containers, executed in the cloud, and assessed based on their ability to predict levels of an individual cell type across samples. In sub-Challenge 1 models will predict coarsely-defined stromal populations, while in sub-Challenge 2 models will further dissect these into subsets (e.g., of T-cell subtypes). Results: We have isolated cell populations of interest and performed quality control on datasets for use in the leaderboard phase. We have defined mixing proportions to assess sensitivity and specificity of deconvolution algorithms. The infrastructure for conducting the Challenge is in place. There is robust interest, with 240 participants pre-registered. The active phase will launch in early 2019 for seven weeks. At its completion, we will identify features of best performing models and provide guidelines where improvements are needed. Discussion: We expect methods to have difficulty deconvolving cell types with closely-correlated expression profiles and in detecting low-frequency populations. An assessment of these limits will enable appropriate use of deconvolution methods in prognostic and predictive models. Our synthetic admixtures model a diverse range of stromal microenvironments that mimic realistic tumors as far as possible. In a subsequent in vivo Challenge, methods will be tested against a future data set in which patient samples are profiled in bulk for deconvolution and at the single-cell level to establish ground truth. Citation Format: Brian S. White, Andrew J. Gentles, Aurélien de Reyniès, Aaron M. Newman, Andrew Lamb, Laura Heiser, Joshua J. Waterfall, Thomas Yu, Justin Guinney. A tumor deconvolution DREAM Challenge: Inferring immune infiltration from bulk gene expression data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1690.

A Critical Analysis of FDA Guidance for User Percentile Device Design Criteria Versus Currently Available Human Factors Engineering Data Sources and Industry Best Practices

A staggering report was recently released by the International Consortium of Investigative Journalists (ICIJ) indicating that 83,000 deaths and 1.7 million injuries were linked to medical device adverse events reported over the last decade (Diaz-Struck, 2018). Furthermore, a 2013 report by McKinsey & Company suggested that such adverse events and associated quality issues cost the medical device industry between $2.5 billion and $5 billion per year on average (Fuhr, George, & Pai, 2013). There is also indication that some aspects of both medical device human factors engineering (HFE) design and Food and Drug Administration (FDA) HFE validation criteria may not provide proper methodologies for selecting and testing patient populations. This may lead medical devices that pass FDA HFE validation to still exhibit significant use errors when utilized by actual patients in the real world. This paper explores the possible sources of HFE performance problems with medical devices and links potential problems to HFE validation methodologies within the FDA HFE guidance framework. The paper also examines device design teams’ failure to utilize HFE best practices in the design and testing of medical devices. Critical conclusions drawn from this analysis suggest that, currently, medical devices may not be designed and validated according to the full percentile range recommended by FDA guidance and HFE best practice. The analysis also indicates that both the FDA and medical device design teams have failed to understand that designing devices to meet a specific range of user needs includes not only physical attributes, but also a wide range of percentile-based cognitive and physiological HFE performance variables. This problem is potentially causing patients who reside at the extremes of percentile distributions (i.e., those with extreme, but acceptable degrees of functional and/or cognitive limitations) to not be able to use devices successfully, which may significantly impact clinical outcomes. The analysis additionally indicates that such failure to design for proper percentile extremes is related to respondent recruiting methods and, more generally, a lack of awareness regarding HFE theory and best practice. Furthermore, for certain categories of modern medical devices, including drug delivery devices, little to no validated HFE percentile-based device design data exists to facilitate medical device development. These issues are not limited to the design of medical devices, but also apply to the design of secondary components of the device system, including device packaging, labeling and instructions for use. To resolve such issues, the authors propose generation of an HFE medical device design criteria dataset for use by development teams during design and testing. The FDA may also reference the proposed dataset during HFE review and validation.

AltEx: An open source web application and toolkit for accessing and exploring altimetry datasets

Understanding the spatial and temporal distribution of hydrologic variables, such as streamflow, is important for sustainable development, especially with global population growth and climate variations. Typical monitoring of streamflow is conducted using in situ gauging stations; however, stations are costly to setup and maintain, leading to data gaps in regions that cannot afford gauges. Satellite data, including altimetry data, are used to supplement in situ observations and in some cases supply information where they are lacking. This study introduces an open-source web application to access and explore altimetry datasets for use in water level monitoring, named the Altimetry Explorer (AltEx). This web application, along with its relevant REST API, facilitates access to altimetry data for analysis, visualization, and impact. The data provided through AltEx is validated using thirteen gauges in the Amazon Basin from 2008 to 2018 with an average Nash-Sutcliffe Coefficient and root mean square error of 0.78 and 1.2 m, respectively. Access to global water level data should be particularly helpful for water resource practitioners and researchers seeking to understand the long-term trends and dynamics of global water level and availability. This work provides an initial framework for a more robust and comprehensive platform to access future altimetry datasets and support research related to global water resources.

MusatransSSRDB (a transcriptome derived SSR database) – An advanced tool for banana improvement

Availability of transcriptome datasets for use in accelerated molecular-based breeding in Musa species is limited. Illumina Hiseq technology was employed to determine differential gene expression between the contrasting cultivars for three different stresses (Eumusae leaf spot -Mycosphaerella eumusae, root lesion nematode - Pratylenchus coffeae and moisture deficit stress) under challenged and unchallenged conditions. An average of 34.72 million of reads was assembled into ~47629 contigs, and ~5,466 simple sequence repeats (SSR) from each library were identified. GO annotation and KEGG pathway analysis were carried for all the transcripts and the SSR, SNPs were also detected. Based on this information, a MusatransSSRDB has been developed. Currently, the database consists of 32,800 SSRs with the unique information like putative function of the SSR-containing genes and their metabolic pathway and expression profiling under various stress conditions. This database provides information on in silico polymorphic SSRs (2830 SSRs) between the contrasting cultivars for each stress and within stress. Information on in silico polymorphic SSRs specific to differentially expressed genes under challenged condition for each stress can also be accessed. This database facilitates the retrieval of results by navigating the tabs for cultivars, stress and polymorphism. This database was developed using HTML, Java and PHP; datasets are stored in MySQL database and accessible in the public domain (http://bioinfnrcb.byethost7.com/nrcbbio/). This unique information facilitates the banana breeder to select the SSR primers based on specific objectives. MusatransSSRDB along with other genomics databases will facilitate the genetic dissection and breeding for complex traits in banana. Thus, this database is a step forward in economizing cost, time, manpower and other resources. Keywords.

Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration

Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist. To develop DL techniques for synthesizing high-resolution realistic fundus images serving as proxy data sets for use by retinal specialists and DL machines. Generative adversarial networks were trained on 133 821 color fundus images from 4613 study participants from the Age-Related Eye Disease Study (AREDS), generating synthetic fundus images with and without AMD. We compared retinal specialists' ability to diagnose AMD on both real and synthetic images, asking them to assess image gradability and testing their ability to discern real from synthetic images. The performance of AMD diagnostic DCNNs (referable vs not referable AMD) trained on either all-real vs all-synthetic data sets was compared. Accuracy of 2 retinal specialists (T.Y.A.L. and K.D.P.) for diagnosing and distinguishing AMD on real vs synthetic images and diagnostic performance (area under the curve) of DL algorithms trained on synthetic vs real images. The diagnostic accuracy of 2 retinal specialists on real vs synthetic images was similar. The accuracy of diagnosis as referable vs nonreferable AMD compared with certified human graders for retinal specialist 1 was 84.54% (error margin, 4.06%) on real images vs 84.12% (error margin, 4.16%) on synthetic images and for retinal specialist 2 was 89.47% (error margin, 3.45%) on real images vs 89.19% (error margin, 3.54%) on synthetic images. Retinal specialists could not distinguish real from synthetic images, with an accuracy of 59.50% (error margin, 3.93%) for retinal specialist 1 and 53.67% (error margin, 3.99%) for retinal specialist 2. The DCNNs trained on real data showed an area under the curve of 0.9706 (error margin, 0.0029), and those trained on synthetic data showed an area under the curve of 0.9235 (error margin, 0.0045). Deep learning-synthesized images appeared to be realistic to retinal specialists, and DCNNs achieved diagnostic performance on synthetic data close to that for real images, suggesting that DL generative techniques hold promise for training humans and machines.

On the Generation of Anomaly Detection Datasets in Industrial Control Systems

In recent decades, Industrial Control Systems (ICS) have been affected by heterogeneous cyberattacks that have a huge impact on the physical world and the people’s safety. Nowadays, the techniques achieving the best performance in the detection of cyber anomalies are based on Machine Learning and, more recently, Deep Learning. Due to the incipient stage of cybersecurity research in ICS, the availability of datasets enabling the evaluation of anomaly detection techniques is insufficient. In this paper, we propose a methodology to generate reliable anomaly detection datasets in ICS that consists of four steps: attacks selection, attacks deployment, traffic capture and features computation. The proposed methodology has been used to generate the Electra Dataset, whose main goal is the evaluation of cybersecurity techniques in an electric traction substation used in the railway industry. Using the Electra dataset, we train several Machine Learning and Deep Learning models to detect anomalies in ICS and the performed experiments show that the models have high precision and, therefore, demonstrate the suitability of our dataset for use in production systems.

The development of food image detection and recognition model of Korean food for mobile dietary management.

BACKGROUND/OBJECTIVESThe aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake.SUBJECTS/METHODSWe collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of 150 × 150 and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition.RESULTSOur complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks.CONCLUSIONThe results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.

Suitability of gridded climate datasets for use in environmental epidemiology.

Epidemiologic analyses of the health effects of meteorological exposures typically rely on observations from the nearest weather station to assess exposure for geographically diverse populations. Gridded climate datasets (GCD) provide spatially resolved weather data that may offer improved exposure estimates, but have not been systematically validated for use in epidemiologic evaluations. As a validation, we linearly regressed daily weather estimates from two GCDs, PRISM and Daymet, to observations from a sample of weather stations across the conterminous United States and compared spatially resolved, population-weighted county average temperatures and heat indices from PRISM to single-pixel PRISM values at the weather stations to identify differences. We found that both Daymet and PRISM accurately estimate ambient temperature and mean heat index at sampled weather stations, but PRISM outperforms Daymet for assessments of humidity and maximum daily heat index. Moreover, spatially-resolved exposure estimates differ from point-based assessments, but with substantial intercounty heterogeneity. We conclude that GCDs offer a potentially useful approach to exposure assessment of meteorological variables that may, in some locations, reduce exposure measurement error, as well as permit assessment of populations distributed far from weather stations.

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.

To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-space datasets for use in stroke imaging. Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q-space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q-space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel-wise machine learning based approaches for q-space imaging. Network-generated images were used to predict stroke functional outcome measures. The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post-stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1-6% of the clinical evaluations. Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q-space data were improved compared to other state-of-the-art methods providing a 10-fold reduction scan time compared to conventional methods.

ERS statement on harmonised standards for lung cancer registration and lung cancer services in Europe.

The European Respiratory Society (ERS) task force for harmonised standards for lung cancer registration and lung cancer services in Europe recognised the need to create a single dataset for use in pan-European data collection and a manual of standards for European lung cancer services.The multidisciplinary task force considered evidence from two different sources, reviewing existing national and international datasets alongside the results of a survey of clinical data collection on lung cancer in 35 European countries. A similar process was followed for the manual of lung cancer services, with the task force using existing guidelines and national or international recommendations for lung cancer services to develop a manual of standards for services in Europe.The task force developed essential and minimum datasets for lung cancer registration to enable all countries to collect the same essential data and some to collect data with greater detail. The task force also developed a manual specifying standards for lung cancer services in Europe.Despite the wide variation in the sociopolitical landscape across Europe, the ERS is determined to encourage the delivery of high-quality lung cancer care. Both the manual of lung cancer services and the minimum dataset for lung cancer registration will support this aspiration.

Trashcano: Developing a quantitative teaching tool to understand ballistics accelerated by explosive volcanic eruptions

Accurate predictions of volcanological phenomena, such as the trajectory of blocks accelerated by volcanic explosions, require quantitative skills training. Large outdoor experiments can be useful to convey concepts of volcanic processes to students in an exciting way. Beyond the fun aspects, these experiments provide an opportunity to engage with the physics of projectile flight and help promote mathematical learning within the Earth Sciences. We present a quantitative framework required to interpret ballistic trajectories and the outdoor experiment known commonly as “trashcano”, taking a step-by-step approach to the physics of this problem, and deriving a range of mathematical solutions involving different levels of complexity. Our solutions are consistent with the predictions from established computer programs for volcanic ballistic trajectory modelling, but we additionally provide a nested set of simplified solutions for a range of teaching scenarios as well as simulated datasets for use where the full experiment may not be possible.

The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems

Abstract. The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)

We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing ( ∼ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community. Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time. The JRA55-do dataset extends from 1958 to the present, with updates expected at least annually. This paper details the adjustments to the original JRA-55 fields, the scientific rationale for these adjustments, and the evaluation of JRA55-do. The adjustments successfully corrected the biases in the original JRA-55 fields. The globally averaged features are similar between the JRA55-do and CORE datasets, implying that JRA55-do can suitably replace the CORE dataset for use in driving global ocean–sea-ice models.

WhatApp Doc? A First Look at WhatsApp Public Group Data

In this dataset paper we describe our work on the collection and analysis of public WhatsApp group data. Our primary goal is to explore the feasibility of collecting and using WhatsApp data for social science research. We therefore present a generalisable data collection methodology, and a publicly available dataset for use by other researchers. To provide context, we perform statistical exploration to allow researchers to understand what public WhatsApp group data can be collected and how this data can be used. Given the widespread use of WhatsApp, our techniques to obtain public data and potential applications are important for the community.

Effects of volume and topographic parameters on rockfall travel distance: a case study from NW Himalayas, Pakistan

This paper presents an analysis of rockfall travel distance for incidents along the roads of the Abbottabad and Mansehra Districts, Khyber Pakhtunkhwa, Pakistan. Data for 121 rockfall events ranging from 100 m 3 to 10 000 m 3 volume were gathered during field investigations and used to facilitate an assessment of parameters such as height of fall, travel angle, travel distance and rockfall volume. The relationship between the height of fall and travel distance was investigated using well-established empirical approaches published in the literature. This analysis showed that travel angle decreased with increase in the rockfall volume, but that there was a weak correlation between the volume of rockfall and travel distance. A strong positive relationship between the travel distance and the height of fall was also identified. Comparison of the rockfall events identified during this study and existing data published from other regions in the world shows that the characteristics of the rockfalls are largely consistent with those identified by previous investigations. The study findings provide a useful set of data for empirical analyses that may be used for hazard and risk assessment in the region.