Achievable Data Quality Research Articles

COVID-19 vaccine and test data management – practice report from a university hospital

Abstract Issue The COVID-19 pandemic generated the urgency for creating and implementing new tools, specifically for data collection, sharing. The data governance of healthcare workers (HCW) was layered on top of pre-existing digitalisation issues in health authorities as well as in health facilities. Description of the problem The vaccination act for the German healthcare sector adopted in December 2021, established the compulsory report of HCWs’ COVID-19 vaccination status. The LMU University Hospital management required an overview of the COVID-19 status (illness, isolation, vaccination, infection) of HCWs in order to ensure continuous clinical service. For the timely and accurate report, it was necessary to create a centralised real-time overview of data that were collected and managed by decentralised structures. Results We connected five in-hospital systems into one platform that was being synced three times a day. For each HCW, we created a dataset with three main categories: core personnel data, COVID-19 status and reason for absence. Two user groups were defined: strategic management (1) and operational management (2), where users of group 2 were only given access to their own unit and were required to update and verify data of their team. Overall, the platform contained data of 14,419 HCWs and had 656 users (Ngroup1 = 16, Ngroup2 = 640). Lessons The data merger revealed issues in interoperability and accuracy and thus expedited data standardisation. The decentralised approach facilitated the operational efficiency, digitalisation, service delivery and managerial empowerment at the hospital. The main challenges pertained to data accuracy and to the continuously changing regulatory requirements as well as privacy protection. As all data were previously hosted on different platforms, an exhaustive process of cleansing was needed prior to merging. Each HCW was assigned to an organisational unit with a respective responsible person in order to achieve data quality. Key messages • Dedicated digital human resources solutions in a university hospital pose a specific challenge for the balance between data privacy, availability and interoperability. • To enable a centralised information display, data must be collected where it originates and made available through short paths. Our solution united decentralised collection and centralised management.

THE EUROSDR RPAS BENCHMARK: OPEN DATASET DESCRIPTION AND SUMMARY OF KEY RESULTS

Abstract. In 2021 EuroSDR initiated a benchmark study with the aim to evaluate the geometric quality of real-world survey data generated from state-of-practice commercial Remotely Piloted Aircraft System (RPAS) photogrammetry (including DJI P4 RTK and DJI P1) and lidar (including DJI L1 and Riegl MiniVUX). The particular benchmark focus was on achievable data quality from real-world network configurations in the absence of ground control, on-the-fly Real Time Kinematic (RTK) corrections, and/or local GNSS base station information. Successive custom datasets were released to registered benchmark participants who submitted individual outputs that were independently evaluated against reference surveys. Without the inclusion of any supporting ground information, DJI P4 RTK and DJI P1 RPAS solutions were found to deliver m- and dm-level accuracies, respectively, in both plan and height. RTK solutions were found to provide cm-level precisions and accuracies, with some outliers. The introduction of ground control points resulted in similar planimetric accuracy to the RTK solutions, but with slight improvements in height. In terms of lidar datasets, the Riegl MiniVUX solution, using corrections from a local base station, was found to provide smaller discrepancies than the DJI L1 RTK solution, when independently compared against terrestrial laser scanning surveys. This paper provides various quality statistics and demonstrates multiple ways of assessing the geometric quality of RPAS data. The EuroSDR RPAS benchmark datasets are now openly available online in order to support and facilitate further investigation by the community.

Updating digital twins: Methodology for data accuracy quality control using machine learning techniques

The Digital Twin (DT) constitutes an integration between cyber and physical spaces and has recently become a popular concept in smart manufacturing and Industry 4.0. The related literature provides a DT characterisation and identifies the problem of updating DT models throughout the product life cycle as one of the knowledge gaps. The DT must update its performance by analysing the variable data in real time of the physical asset, whose behaviour is constantly changing over time. The automatic update process involves a data quality problem, i.e., ensuring that the captured values do not come from measurement or provoked errors. In this work, a novel methodology has been proposed to achieve data quality in the interconnection between digital and physical spaces. The methodology is applied to a real case study using the DT of a real solar cooling plant, acting as a learning decision support system that ensures the quality of the data during the update of the DT. The implementation of the methodology integrates a neurofuzzy system to detect failures and a recurrent neural network to predict the size of the errors. Experiments were carried out using historical plant data that showed great results in terms of detection and prediction accuracy, demonstrating the feasibility of applying the methodology in terms of computation time.

نضج إدارة البيانات الرئيسية في المؤسسات الحكومية: دراسة حالة لوزارة النقل والخدمات اللوجستية

ملخص تستمد الدراسة الحالية أهميتها من منطلق أن البيانات بشكل عام تعد عنصرًا مهمًّا في كل مؤسسة، وركيزة أساسية في اتخاذ القرارات، بالإضافة إلى الدور الذي يقوم به كلٌّ من البعد التنظيمي والتكنولوجي والإداري في تحسين جودة البيانات الرئيسة. وتهدف الدراسة إلى التعرف على الأبعاد الثلاثة (الإدارية – التقنية – البشرية) التي تؤثر في نضج إدارة البيانات الرئيسة في المؤسسات الحكومية بشكل عام، ووزارة النقل والخدمات اللوجستية بشكل خاص، وبناء على ذلك اعتمدت الدراسة على منهج تحليل المحتوى لتحليل الإنتاج الفكري المنشور، كما اعتمدت الدراسة على منهج دراسة الحالة بغرض الوصول إلى فهم أعمق عن مدى نضج إدارة البيانات الرئيسة في وزارة النقل والخدمات اللوجستية في المملكة العربية السعودية، واستطاعت الدراسة وضع نموذج مقترح لما ينبغي أخذه بعين الاعتبار، سواء من الباحثين عند تصميم حلول مماثلة، أو من المسؤولين عند التخطيط لتطويع إدارة البيانات الرئيسة في مؤسساتهم، بحيث تكون فعالة للغاية. أهم ما توصلت إليه الدراسة: تبنت الوزارة واجهة رسمية معتمدة لتبادل البيانات داخل مختلف الإدارات في الوزارة بشكل منظم يُسهل الحصول على البيانات عند الحاجة إليها. وتفحص الوزارة قواعد البيانات لديها باستمرار لتحسين أداء الأعمال وكفاءته، كما تخزن البيانات بطريقة فعّالة تُمكنها من الاستفادة من تلك البيانات للتنبؤ والتحليل لاتخاذ القرارات. كما تفتقر الوزارة إلى وجود مستودع مركزي يسهل الوصول إلى البيانات الرئيسة المشتركة في مختلف الإدارات العامة. وخرجت الدراسة بوضع نموذج مقترح لإدارة البيانات الرئيسة في وزارة النقل والخدمات اللوجستية، وقدمت مجموعة من التوصيات، أهمها: الحرص على وجود نظام مقياس معياري لتقويم جودة البيانات لدى الوزارة لضمان استمرار جودتها. وضرورة استحداث مستودع مركزي لتخزين البيانات الرئيسة وبثها عبر مختلف الإدارات، وتسهيل وصول الموظفين إليها لأداء الأعمال. Data have become significant within institutions and fundamental when making decisions. To achieve data quality, master data management is crucial, which in turn depends on some organizational, technological, and administrative dimensions. This study aims to identify the three dimensions (administrative, technological, and human) that affect the maturity of the master data management in government institutions, in particular the Ministry of Transport and Logistic Services (Ministry of TLS) in Saudi Arabia. The study utilized content analysis of existing related published studies and also the case-study method was selected to allow for a deeper understanding of the maturity extent of the master data management (e.g. the Ministry of TLS in Saudi Arabia). The study developed a proposed model that can be highly effective and useful for researchers or officials when designing or incorporating master data management. This research found out that the Ministry of TLS has adopted an official, approved data-exchange interface across several departments to obtain data in an organized manner when needed. In addition, the Ministry is constantly checking its databases to improve work performance and efficiency.Lastly, The Ministry lacks a central data-storage repository to allow for cross departmental access to one unified master data. The paper concluded with a number of recommendations, the most important of which is introducing a tool at the Ministry of TLS for assessing and maintaining regularly its data quality and the necessity of setting up a central data repository.

To save from oblivion: Comparative analysis of remote sensing means of documenting forgotten architectural treasures – Zagórz Monastery complex, Poland

Technological development of remote sensing and computer graphic techniques resulted in various methods of documenting heritage sites. This paper presents validation of point clouds produced using different measurement techniques: SfM (based on images taken with a hand-held digital camera and from a UAV) and TLS and its products – a textured 3D vector model and a reality mesh. The study area is the ruins of the Church of the Annunciation of the Blessed Virgin Mary of Mount Carmel in Zagórz, Poland. The obtained results allowed for formulating general conclusions regarding the usage of particular methods for documenting heritage sites, achievable measurement accuracies and data quality. Possible further applications of produced models in VR and BIM were discussed. Based on the analyses, the research also proposes workflow for inventory of heritage sites, which may become useful during documentation surveys and modelling planning.

Classification of real-world pathological phonocardiograms through multi-instance learning.

Heart auscultation is an inexpensive and fundamental technique to effectively to diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel e.g. in developing countries, a large body of research is attempting to develop automated, computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety o possible heart pathologies, and a generally poor signal-to-noise ratio make this problem extremely challenging. We present an accurate classification strategy for diagnosing heart sounds based on 1) automatic heart phase segmentation, 2) state-of-the art filters drawn from the filed of speech synthesis (mel-frequency cepstral representation), and 3) an ad-hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase, hence leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an AUC of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heart sound classification, especially as a screening tool in a variety of situations including telemedicine applications.

A novel multi-branch architecture for state oftheartrobust detection ofpathological phonocardiograms.

Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel, e.g. in developing countries, many efforts are made worldwide to propose computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety of possible heart pathologies, and a generally poor signal-to-noise ratio make this problem very challenging. We present an accurate classification strategy for diagnosing heart sounds based on (1) automatic heart phase segmentation, (2) state-of-the art filters drawn from the field of speech synthesis (mel-frequency cepstral representation) and (3) an ad hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase hence implicitly leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an area under the curve of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heartsound classification, especially as a screening tool in a variety of situations including telemedicine applications. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals.

A key access point to the functioning of the autonomic nervous system is the investigation of peripheral signals. Wearable devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. However, the achievable data quality can be lower, and data are subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study, we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and electrodermal activity signals is validated with a standard set of signal quality indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of six different physiological measures collected from 18 subjects with WDs. This study indicates the need to validate the use of WDs in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducible results.

Data journals: types of peer review, review criteria, and editorial committee members’ positions

Purpose: This study analyzed the peer review systems, criteria, and editorial committee structures of data journals, aiming to determine the current state of data peer review and to offer suggestions.Methods: We analyzed peer review systems and criteria for peer review in nine data journals indexed by Web of Science, as well as the positions of the editorial committee members of the journals. Each data journal’s website was initially surveyed, and the editors-in-chief were queried via email about any information not found on the websites. The peer review criteria of the journals were analyzed in terms of data quality, metadata quality, and general quality.Results: Seven of the nine data journals adopted single-blind and open review peer review methods. The remaining two implemented modified models, such as interactive and community review. In the peer review criteria, there was a shared emphasis on the appropriateness of data production methodology and detailed descriptions. The editorial committees of the journals tended to have subject editors or subject advisory boards, while a few journals included positions with the responsibility of evaluating the technical quality of data.Conclusion: Creating a community of subject experts and securing various editorial positions for peer review are necessary for data journals to achieve data quality assurance and to promote reuse. New practices will emerge in terms of data peer review models, criteria, and editorial positions, and further research needs to be conducted.

A new database of the references on international clinical practice guidelines: a facility for the evaluation of clinical research

Although there are now several bibliographic databases of research publications, such as Google Scholar, Pubmed, Scopus, and the Web of Science (WoS), and some also include counts of citations, there is at present no similarly comprehensive database of the rapidly growing number of clinical practice guidelines (CPGs), with their references, which sometimes number in the hundreds. CPGs have been shown to be useful for the evaluation of clinical (as opposed to basic) biomedical research, which often suffers from relatively low counts of citations in the serial literature. The objectives were to introduce a new citation database, clinical impact®, and demonstrate how it can be used to evaluate research impact of clinical research publications by exploring the characteristics of CPG citations of two sets of papers, as well as show temporal variation of clinical impact® and the WoS. The paper includes the methodology used to retain the data and also the rationale adopted to achieve data quality. The analysis showed that although CPGs tend preferentially to cite papers from their own country, this is not always the case. It also showed that cited papers tend to have a more clinical research level than uncited papers. An analysis of diachronous citations in both clinical impact® and the WoS showed that although the WoS citations showed a decreasing trend after a peak at 2–3 years after publication, this was less clear for CPG citations and a longer timescale would be needed to evaluate their impact on these documents.

Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning.

Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision “digital twin”, in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and digital assets at several different system layers. However, the inherent limitations of sampling and observing physical attributes often pose issues related to data uncertainty in practice. In this paper, we propose a learning-based data management scheme where the implementation is layered between sensors attached to physical attributes and domain-specific applications, thereby mitigating the data uncertainty between them. To do so, we present a sensor data management framework, namely D2WIN, which adopts reinforcement learning (RL) techniques to manage the data quality for CPS applications and autonomous systems. To deal with the scale issue incurred by many physical attributes and sensor streams when adopting RL, we propose an action embedding strategy that exploits their distance-based similarity in the physical space coordination. We introduce two embedding methods, i.e., a user-defined function and a generative model, for different conditions. Through experiments, we demonstrate that the D2WIN framework with the action embedding outperforms several known heuristics in terms of achievable data quality under certain resource restrictions. We also test the framework with an autonomous driving simulator, clearly showing its benefit. For example, with only 30% of updates selectively applied by the learned policy, the driving agent maintains its performance about 96.2%, as compared to the ideal condition with full updates.

A Cross-Disciplinary Training Program for the Advancement of Medical Countermeasures.

In September 2012, the United States Department of Health and Human Services Food and Drug Administration's (FDA) Office of Counterterrorism and Emerging Threats and the University of Texas Medical Branch at Galveston entered into a collaborative educational partnership for the academic development of a robust training program for good laboratory practices in high-biocontainment environments. The implementation of problem-based learning techniques encouraged researchers and regulators to cross-educate each other on the challenges related to the conduct of regulated studies in biological safety level 4 (BSL-4) laboratories and identified solutions that were acceptable from scientific and regulatory perspectives. The result was the development of a face-to-face course entitled Achieving Data Quality and Integrity in Maximum Containment Laboratories and an additional online companion course covering the FDA regulation, Good Laboratory Practice for Nonclinical Laboratory Studies (21 CFR Part 58). The course offers a unique opportunity for members of the regulatory and scientific communities to solve complex issues in an interactive educational environment, especially for the advancement of medical countermeasures (MCMs) via the FDA Animal Rule (21 CFR Parts 314 and 601 (2002)). The program occurs annually and is expanding in 2019 to include a course addressing data quality and integrity in clinical trials involving the evaluation of MCMs for high-consequence pathogens. To date, 311 individuals have attended the course. Based on attendance numbers, diversity of participation (by affiliation and area of expertise), and self-reported evaluation results, course attendees indicate that the training program addresses a knowledge gap and that they will implement knowledge gained.

Quality Control of Microwave Equipment for Tissue Imaging

While the development of microwave imaging technology for biomedical applications has been ongoing for many years, no clinical devices are currently in use. A major challenge is achieving data quality that would ensure adequate image resolution for the specific diagnostic application. Imaging systems are typically designed with a theoretical resolution limit in mind, which is rarely achieved in practice due to measurement uncertainties, background clutter, and system noise. Uncertainties and background clutter are particularly prominent in medical diagnostic imaging. This paper proposes a method for data quality assessment of an experimental imaging system that aims at a specific image resolution. It utilizes two measurements, one of a uniform background medium and one of the same medium with a small scattering probe embedded within it. The probe's size and permittivity reflect the desired application-specific resolution. The method extracts the system point-spread function (PSF) from the two measurements and computes the PSF contrast-to-noise ratio. A case study is presented, demonstrating the quality control protocol and its ability to identify datasets of inadequate quality and provide an evaluation metric. The protocol also highlights possible sources of error and enables data filtering that increases significantly the reconstructed image quality.

Exploring incentive mechanisms for mobile crowdsourcing: sense of safety in a Thai city

ABSTRACTThe rapid adoption of mobile devices enables capture and transmission of a variety of sensor and user-contributed data, creating a new data collection paradigm and a wide range of services, often termed as mobile crowdsourcing. However, we are still investigating factors contributing towards the effectiveness of such systems. One key factor to succeed in mobile crowdsourcing applications is the incentive mechanism, which motivates people to contribute to a crowdsourcing effort. In this research, we conducted field experiments that compared the effectiveness of non-monetary and monetary incentive mechanisms, using both quantitative and qualitative methods. The focus was on an exploration of how these mechanisms motivate users’ performance in a mobile crowdsourcing environment. In the experiment, we developed a smart zoning application that allows users to share areas of the cities they perceived as safe or unsafe. The results from this study contribute to research in mobile crowdsourcing for urban understanding. Taken together, these results suggest that payment-related incentives can not only engage people’s interest in project participation but also help improve their work performance in terms of productivity and quality. Notably, a fixed-price financial reward mechanism is best suited for short period data collection and achieving data quality. Also, referral incentive mechanisms, if properly designed, have the potential to extend user coverage, both spatially and temporally. These results can be helpful with regard to the formulation of guidelines on how to create and organize effective payment-based incentives for crowd involvement in cities.

SDSS-IV MaNGA IFS GALAXY SURVEY—SURVEY DESIGN, EXECUTION, AND INITIAL DATA QUALITY

ABSTRACT The MaNGA Survey (Mapping Nearby Galaxies at Apache Point Observatory) is one of three core programs in the Sloan Digital Sky Survey IV. It is obtaining integral field spectroscopy for 10,000 nearby galaxies at a spectral resolution of R ∼ 2000 from 3622 to 10354 Å. The design of the survey is driven by a set of science requirements on the precision of estimates of the following properties: star formation rate surface density, gas metallicity, stellar population age, metallicity, and abundance ratio, and their gradients; stellar and gas kinematics; and enclosed gravitational mass as a function of radius. We describe how these science requirements set the depth of the observations and dictate sample selection. The majority of targeted galaxies are selected to ensure uniform spatial coverage in units of effective radius (R e ) while maximizing spatial resolution. About two-thirds of the sample is covered out to 1.5R e (Primary sample), and one-third of the sample is covered to 2.5R e (Secondary sample). We describe the survey execution with details that would be useful in the design of similar future surveys. We also present statistics on the achieved data quality, specifically the point-spread function, sampling uniformity, spectral resolution, sky subtraction, and flux calibration. For our Primary sample, the median r-band signal-to-noise ratio is ∼70 per 1.4 Å pixel for spectra stacked between 1R e and 1.5R e . Measurements of various galaxy properties from the first-year data show that we are meeting or exceeding the defined requirements for the majority of our science goals.

Structure from motion photogrammetry in physical geography

Accurate, precise and rapid acquisition of topographic data is fundamental to many sub-disciplines of physical geography. Technological developments over the past few decades have made fully distributed data sets of centimetric resolution and accuracy commonplace, yet the emergence of Structure from Motion (SfM) with Multi-View Stereo (MVS) in recent years has revolutionised three-dimensional topographic surveys in physical geography by democratising data collection and processing. SfM-MVS originates from the fields of computer vision and photogrammetry, requires minimal expensive equipment or specialist expertise and, under certain conditions, can produce point clouds of comparable quality to existing survey methods (e.g. Terrestrial Laser Scanning). Consequently, applications of SfM-MVS in physical geography have multiplied rapidly. There are many practical options available to physical geographers when planning a SfM-MVS survey (e.g. platforms, cameras, software), yet, many SfM-MVS end-users are uncertain as to the errors associated with each choice and, perhaps most fundamentally, the processes actually taking place as part of the SfM-MVS workflow. This paper details the typical workflow applied by SfM-MVS software packages, reviews practical details of implementing SfM-MVS, combines existing validation studies to assess practically achievable data quality and reviews the range of applications of SfM-MVS in physical geography. The flexibility of the SfM-MVS approach complicates attempts to validate SfM-MVS robustly as each individual validation study will use a different approach (e.g. platform, camera, georeferencing method, etc.). We highlight the need for greater transparency in SfM-MVS processing and enhanced ability to adjust parameters that determine survey quality. Looking forwards, future prospects of SfM-MVS in physical geography are identified through discussion of more recent developments in the fields of image analysis and computer vision.

The first and second data releases of the Kilo-Degree Survey

The Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will image 1500 square degrees in four filters (ugri), and together with its near-infrared counterpart VIKING will produce deep photometry in nine bands. Designed for weak lensing shape and photometric redshift measurements, the core science driver of the survey is mapping the large-scale matter distribution in the Universe back to a redshift of ~0.5. Secondary science cases are manifold, covering topics such as galaxy evolution, Milky Way structure, and the detection of high-redshift clusters and quasars. KiDS is an ESO Public Survey and dedicated to serving the astronomical community with high-quality data products derived from the survey data, as well as with calibration data. Public data releases will be made on a yearly basis, the first two of which are presented here. For a total of 148 survey tiles (~160 sq.deg.) astrometrically and photometrically calibrated, coadded ugri images have been released, accompanied by weight maps, masks, source lists, and a multi-band source catalog. A dedicated pipeline and data management system based on the Astro-WISE software system, combined with newly developed masking and source classification software, is used for the data production of the data products described here. The achieved data quality and early science projects based on the data products in the first two data releases are reviewed in order to validate the survey data. Early scientific results include the detection of nine high-z QSOs, fifteen candidate strong gravitational lenses, high-quality photometric redshifts and galaxy structural parameters for hundreds of thousands of galaxies. (Abridged)

Quick scanning monochromator for millisecond in situ and in operando X-ray absorption spectroscopy.

The design and capabilities of a novel Quick scanning Extended X-ray Absorption Fine Structure (QEXAFS) monochromator are presented. The oscillatory movement of the crystal stage is realized by means of a unique open-loop driving scheme operating a direct drive torque motor. The entire drive mechanics are installed inside of a goniometer located on the atmospheric side of the vacuum chamber. This design allows remote adjustment of the oscillation frequency and spectral range, giving complete control of QEXAFS measurements. It also features a real step-scanning mode, which operates without a control loop to prevent induced vibrations. Equipped with Si(111) and Si(311) crystals on a single stage, it facilitates an energy range from 4.0 keV to 43 keV. Extended X-ray absorption fine structure spectra up to k = 14.4 Å(-1) have been acquired within 17 ms and X-ray absorption near edge structure spectra covering more than 200 eV within 10 ms. The achieved data quality is excellent as shown by the presented measurements.

Screening and Flagging of Solar Irradiation and Ancillary Meteorological Data

Solar irradiance and ancillary meteorological data is frequently measured by automatic weather stations for use within solar resource assessment for solar power plants. High accuracy measurement data are required for comparison and adjustment of satellite data and derivation of the expectable long-term mean value of the solar resource. Thus, utmost diligence must be taken during the measurement process and data evaluation to achieve data quality required for project financing. The combination of automatic data screening and manual flagging by an expert in at least daily frequency in close collaboration with a local station operator is the most recognizedway to detect impacts on measurement data and paves the way for post-correcting data treatment where necessary and reasonable. This is the preferred and recommended procedure, resulting in highestdata quality. The presented work is also understood as a basis for ongoing development and discussion among the corresponding expert group about screening of irradiance and ancillary meteorological data and its corresponding flagging. A common understanding and wide conformity about the screening process and flagging of data would be aspired.

Technology Focus: Seismic Applications (March 2014)

Technology Focus Some remarkable breakthroughs in data quality have been shared in recent reviews, and the ongoing technical developments in data acquisition and processing remain truly exciting. With an ever-growing toolbox of geophysical technologies, selecting the right technology solution, however, can be challenging. Fueled by many published success stories—for instance, about marine broadband streamer seismic or wide-azimuth surveys combined with reverse time migration—business expectations about achievable data quality and turnaround times are high. At the same time, the industry has to respond to increased public concerns about the effect of seismic activities on the environment. Tighter environmental and safety regulations are evolving for all areas, and, more than ever before, the industry has to demonstrate that seismic operations are managed in a responsible way. Detailed planning and survey design are needed to reduce the operational footprint and to minimize the environmental impact. The effect of sound and seismic measurements on marine life has been and remains a particular focus. Continued joint industry research efforts including environmental studies and marine source technologies have to alleviate public concerns and ensure that the dialogue with the public and regulators is supported by adequate scientific evidence. Scalable data-acquisition solutions are essential to meeting the high demands on data quality, survey productivity, reduced environmental impact, and cost. Scalable technologies enable smart, flexible, and cost-efficient survey designs that cope with the operational conditions and environmental constraints in the field while still meeting all processing and imaging requirements. The optimal balance between source and receiver efforts will always be a key design parameter, though the actual ratio obviously will vary with changing environments, survey sizes, and acquisition techniques. Ocean-bottom seismic surveys with cables or autonomous nodes do not yet offer the high channel count of advanced streamer seismic and generally depend on higher source efforts compared with towed-streamer seismic. For onshore surveys, the source density and achievable channel count will largely depend on terrain conditions, and solutions will differ between open desert environments and more challenging terrains with limited access. Against this background, the papers selected for this review should provide interesting examples for exploration, development, or reservoir monitoring in various settings and terrains. The commonality in the selected papers is the ability to tailor technology solutions to the dominating technical, operational, and environmental challenges, and to the prevailing business challenges. A career in geophysics has never been as integrated, as challenging, as interesting, and as rewarding as it is today. JPT Recommended additional reading at OnePetro: www.onepetro.org. IPTC 16907 Seismic-Velocity Uncertainty Analysis in an Underexplored Basin by R. Bacenetti, Eni, et al. IPTC 16804 Variable-Depth Streamer: Benefits for Rock-Property Inversion by L. Michel, CGG Veritas, et al. IPTC 16763 Marine Seismic Acquisition at Scott Reef, Western Australia: Minimizing Environmental Impacts in a Sensitive and Remote Location by Mark Taylor, Woodside Energy, et al.