Quality Of Research Data Research Articles

Sentinel-3A SRAL Global Statistical Assessment and Cross-Calibration with Jason-3

The Sentinel-3A satellite, equipped with Synthetic Aperture Radar (SAR) Altimeter (SRAL) instrument to derive sea surface height, significant wave height and surface wind speed over the global ocean, was launched on 16 February 2016. The assessment of data quality and the system performance of the altimeter are very important to data application. In this article, Sentinel-3A SRAL data quality is assessed and altimetry system performance is estimated by verifying data availability and monitoring the parameters of altimeter and radiometer through the global statistical analyses of Sentinel-3A Non-Time-Critical (NTC) Marine Level 2 products during the period from 13 March 2016 to 25 February 2019, in comparison with self-crossovers and cross-calibration with the Jason-3 mission. The global statistical analyses and the comparisons at self-crossovers show that Sentinel-3A SRAL data and performance are stable and have no trend over time, and the total cycle average root mean square errors (RMSEs) of sea surface height (SSH) differences at self-crossovers is about 5.4 cm. The comparisons at the dual-crossovers show the consistency of the observation between Sentinel-3A SRAL and Jason-3, and indicate that the systemic bias of SSH is about 2.96 cm. In general, it can be concluded that Sentinel-3A SRAL has good and stable data quality and system performance for operational ocean forecasting and scientific research.

Proposed Guideline for Minimum Information Stroke Research and Clinical Data Reporting

The management and analyses of large datasets is one of the grand challenges of modern biomedical research. Establishing methods to harmonise and standardise data collection, reporting, sharing and the employed data dictionaries, can support the resolution of these challenges whilst improving research quality, data quality and integrity, allowing sustainable knowledge transfer through re-usability, interoperability, reproducibility. The current project aimed to develop and propose a standardised reporting guideline for stroke research and clinical data reporting. Through systematic consolidation and harmonization of published data collection and reporting standards, several recommendations were drafted for the proposed guideline. These recommendations were reviewed by domain-researchers and clinicians using an online survey, developed in REDCap. The survey was completed by 20 international stroke-specialists, majority of respondents were based in Africa (10), followed by America, Europe and Australia (10). Of these respondents; the majority were working as dual clinician-researchers (57%) with more than 10 years’ experience in the field (78%). Data elements within the reporting standard were classified as participant-, study- and experiment-level information, further subdivided into essential or optional information, and defined using existing ontologies. The proposed reporting guideline can be employed for research utility and adapted for clinical utility as well. It is accompanied with an associated XML schema for REDCap implementation, to increase the user friendliness of data capturing, sharing, reporting and governance. Ultimately, the adoption of common reporting in stroke research has the potential to ensure that researchers gain the maximum benefit from their generated data and data collections.

Implementing a quality management system using good clinical laboratory practice guidelines at KEMRI-CMR to support medical research.

Background: Good Clinical Laboratory Practice (GCLP) is a standard that helps ensure the quality and reliability of research data through principles of Good Laboratory Practice (GLP) and Good Clinical Practice (GCP). The implementation of GCLP includes careful documentation of procedures, competencies and safety measures. Implementation of GCLP is influenced by existing resources and quality systems, thus laboratories in low- and middle-income countries may face additional challenges. Methods: This paper describes implementation of GCLP at the Kenya Medical Research Institute-Center for Microbiology Research (KEMRI-CMR) as part of a quality system to support medical research. This study employed assessment, twinning (institutional mentorship) model, conducting relevant training workshops and Kaizen 5S approaches to implement an effective quality management system using GCLP standard. This was achieved through a collaboration between the KEMRI/Wellcome Trust Research Programme (KWTRP) and KEMRI-CMR. The aim was compliance and continuous monitoring to meet international GCLP standards in a way that could be replicated in other research organizations. Results: Following a baseline assessment in March 2017, training, mentorship and a cycle of quality audit and corrective action using a Kaizen 5S approach (sorting, setting in order, shining, standardizing and sustaining) was established. Laboratory personnel were trained in writing standard operating procedures and analytical plans, microbiological techniques, and good documentation practice. Mid-term and exit assessments demonstrated significant declines in non-conformances across all GCLP elements. KEMRI-CMR achieved GCLP accreditation in May 2018 by Qualogy Ltd (UK). Conclusions: Involving all the laboratory personnel in implementation of quality management system processes is critical to success. An institutional mentorship (twinning) approach shows potential for future collaborations between accredited and non-accredited organizations to accelerate the implementation of high-quality management systems and continuous improvement.

Transparency and quality assessment of research data in post-Soviet area studies: The potential of an interactive online platform

The social sciences are increasingly addressing the quality of research data and debating ways to improve data transparency, that is, the availability of original research data to corroborate claims made in academic publications. This article offers a systematic discussion of related problems and challenges with the example of post-Soviet area studies. It goes on to examine ways to improve data transparency. Although the Internet has a huge potential for linking research with resulting publications and underlying data as well as for organizing a collective discussion around the research, current data repositories do not truly go beyond basic upload and download functions for datasets. With the example of the Discuss Data project, this article gives an overview of more elaborated features that can easily be implemented to improve the visibility and quality assessment of data collections. Finally, it discusses ethical concerns about data transparency related to privacy protection and copyrights.

Deepen the transparency concept of clinical research and strengthen whole-process supervision and administration of research

Objective To acquire specific suggestions for improving the transparency of clinical research. Methods Summarize main challenges for transparency in clinical research, propose possible solutions. Results Currently the common practice of clinical research transparency is to promote study registration in the beginning phase, and reporting guildlines, data sharing in the end phase of the study. The main challenges identified in clinical research transparency are (1) lacking awareness of transparency among investigators, (2) poor data integrity and traceability of research data resulted by the ignorance of transparency during study implementation process. To face the challenges, investigator should be more proactive and related research administration should be streamlined. Conclusions To improve the transparency of clinical research, better understandings of the concept of clinical research transparency for investigators and whole-process supervision from the administrative aspect should be strengthened. Furthermore, advanced information technology should be adopted to construct a hospital-based source data platform for complete clinical research process management, it helps in promoting research data quality and could become a suitable tool for clinical research administration in hospitals. Ultimately, full research transparency would be achieved. Key words: Clinical research; Transparency; Source data; Process supervision; Traceability

Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia.

Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research. This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia. A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle. Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams. Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group. This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.

Utilization of a Wide Array of Nonvalidated Outcome Scales in Pediatric Orthopaedic Publications: Can't We All Measure the Same Thing?

Clinical changes are best evaluated with standardized, validated outcomes, including both patient-reported outcome measures and surgeon-reported outcome measures (PROMs and SROMs). The purpose of this study was to describe the spectrum of outcome measures used in pediatric orthopaedic publications over the past 10 years and to determine the proportion that are in fact age-appropriate, validated, and appropriately applied in terms of condition and population. The Journal of Bone and Joint Surgery, The Bone and Joint Journal, Journal of Pediatric Orthopaedics A and B, and Journal of Children's Orthopaedics were systematically searched for studies including children aged 18 and below, over a 10-year period from January 2005 to December 2014. Economic evaluations, letters, editorials, review articles, and clinical guidelines were excluded. SROMs and PROMs used were extracted, as were details on subject age and condition for which they were used. Each outcome scale was assessed for validity, and the proportion of scales used appropriately was calculated. Cochrane-Armitage test of trend was used to determine changes in PROM and SROM utilization over the study period. A total of 4614 articles were identified, of which 2251 met inclusion and exclusion criteria. In total, 259 (11.5%) of studies used a PROM, whereas 326 (14.5%) used a SROM. A total of 230 different outcome scales were identified; 115 were patient reported and 115 were surgeon reported. However, only 18.7% of SROMs and 38.3% of PROMs were applied to an age and disease-appropriate demographic. Overall, there was a significant increase in the overall utilization of PROMs during the study period (P=0.004), but no corresponding increase in pediatric-validated PROMs (P=0.164). SROM utilization did not significantly change over the study period (P=0.337). Within the field of pediatric orthopaedics, an expansive variety of outcome scales are used, many of which have not been validated in children. Improved uniformity in reporting of outcomes and use of disease and age-validated outcomes scales is essential to improve multicenter research collaboration and data quality to generate appropriate evidence-based conclusions and treatment strategies in pediatric orthopaedics. Level IV-systematic review.

Quality Governance in Biomedical Research.

Quality research data are essential for quality decision-making and thus for unlocking true innovation potential to ultimately help address unmet medical needs.The factors influencing quality are diverse. They depend on institution type and experiment type and can be of both technical and cultural nature. A well-thought-out governance mechanism will help understand, monitor, and control research data quality in a research institution.In this chapter we provide practical guidance for simple, effective, and sustainable quality governance, tailored to the needs of an organization performing nonregulated preclinical research and owned by all stakeholders.GLP regulations have been developed as a managerial framework under which nonclinical safety testing of pharmaceutical and other products should be conducted. One could argue whether these regulations should be applied to all nonclinical biomedical studies. However, the extensive technical requirements of GLP may not always be fit to the wide variety of studies outside the safety arena and may be seen as overly prescriptive and bureaucratic. In addition, GLP regulations do not take into account scientific excellence in terms of study design or adequacy of analytical methods. For these reasons and in order to allow a lean and fit for purpose approach, the content of this chapter is independent from GLP. Nevertheless, certain topics covered by GLP can be seen as valuable across biomedical research. Examples are focus on transparency and the importance of clear roles and responsibilities for different functions participating in a study.

Implementing a quality management system using good clinical laboratory practice guidelines at KEMRI-CMR to support medical research

Background: Good Clinical Laboratory Practice (GCLP) is a standard that helps ensure the quality and reliability of research data through principles of Good Laboratory Practice (GLP) and Good Clinical Practice (GCP). The implementation of GCLP includes careful documentation of procedures, competencies and safety measures. Implementation of GCLP is influenced by existing resources and quality systems, thus laboratories in low- and middle-income countries may face additional challenges. Methods: This paper describes implementation of Good Clinical Laboratory Practice (GCLP) at the Kenya Medical Research Institute-Center for Microbiology Research (KEMRI-CMR) as part of a quality system to support medical research. This study employed assessment, twinning (institutional mentorship) model, conducting relevant training workshops and Kaizen 5S approaches to implement an effective quality management system using GCLP standard. This was achieved through a collaboration between the KEMRI/Wellcome Trust Research Programme (KWTRP) and KEMRI-CMR. The aim was compliance and continuous monitoring to meet international GCLP standards in a way that could be replicated in other research organizations. Results: Following a baseline assessment in March 2017, training, mentorship and a cycle of quality audit and corrective action using a Kaizen 5S approach (sorting, setting in order, shining, standardizing and sustaining) was established. Laboratory personnel were trained in writing standard operating procedures and analytical plans, microbiological techniques, and good documentation practice. Mid-term and exit assessments demonstrated significant declines in non-conformances across all GCLP elements. KEMRI-CMR achieved GCLP accreditation in May 2018 by Qualogy Ltd (UK). Conclusions: Involving all the laboratory personnel in implementation of quality management system processes is critical to success. An institutional mentorship (twinning) approach shows potential for future collaborations between accredited and non-accredited organizations to accelerate the implementation of high-quality management systems and continuous improvement.

Implementation of Good Clinical Laboratory Practice in an Immunology Basic Research Laboratory: The KEMRI-Wellcome Trust Research Laboratories Experience.

ObjectivesGood Clinical Laboratory Practice (GCLP) is a standard that ensures quality and reliability of research data by adopting the principles of Good Laboratory Practice and Good Clinical Practice. Even though implementing a quality system in a basic research laboratory is still a contentious issue, it ensures that the research data are accurate, valid, and reliable. GCLP implementation requires proper documented procedures and safety precautions to achieve this objective.MethodsThis article describes the Kenya Medical Research Institute (KEMRI)–Wellcome Trust Research Laboratories experience in the implementation of GCLP guidelines in a laboratory conducting basic research.ResultsThe laboratory managed to implement GCLP elements that could be applied to a basic research laboratory, such as standard operating procedures, equipment management, laboratory analytical plans, organization, and personnel. The laboratory achieved GCLP accreditation in October 2015.ConclusionsThe methodology, suggestions, and comments that arose from our experience in implementing GCLP guidelines can be used by other laboratories to develop a quality system using GCLP guidelines to support medical research conducted to ensure the research data are reliable and can be easily reconstructed in other research settings.

How to set up a database?-a five-step process.

Database set-up directly impacts the quality and viability of research data, and therefore is a crucial part of the quality of clinical research. Setting up a quality database implies following a strict data-management process. Too much collected information threatens the quality of the information required to achieve the objectives of the study. Therefore, the data that will be collected and managed have to be cautiously discussed and selected. Case report forms (CRF) are the tools the most frequently used to collect the data specified by the protocol. An informative and well-structured document simplifies database design and data validation. Key elements are about choice of sequential or thematic structuring, information and type of information that should be entered and the importance of data standards and coding guide. Final database must be structured with unique ID patient, with one record per subject or per measure. Specific information must be provided for each variable according to the database specifications. The quality of the results is directly related to the quality of the collected data. The CRF should then be completed as fully and accurately as possible. Data validation relies on three key points: the CRF completion guidelines, the Edit Checks process and the Data clarification process. Various open source or business software applications provide all functionalities to set up a clinical data base and CRF. The General Data Protection Regulation (GDPR) standardizes and strengthens the protection of personal data across the EU and for other country's data being "processed" within the EU. The General principles include lawfulness, fairness and transparency, restricted use of data, data minimization, accuracy, limited storage, confidentiality and probity, and accountability.

S-Pol’s Polarimetric Data Reveal Detailed Storm Features (and Insect Behavior)

AbstractThe National Center for Atmospheric Research (NCAR) operates a state-of-the-art S-band dual-polarization Doppler radar (S-Pol) for the National Science Foundation (NSF). This radar has some similar and some distinguishing characteristics to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler Polarimetric (WSR-88DP). One key difference is that the WSR-88DP is used for operational purposes where rapid 360° volumetric scanning is required to monitor rapid changes in storm characteristics for nowcasting and issuing severe storm warnings. Since S-Pol is used to support the NSF research community, it usually scans at much slower rates than operational radars. This results in higher resolution and higher data quality suitable for many research studies. An important difference between S-Pol and the WSR-88DP is S-Pol’s ability to use customized scan strategies including scanning on vertical surfaces ([range–height indicators (RHIs)], which are presently not done by WSR-88DPs. RHIs provide high-resolution microphysical structures of convective storms, which are central to many research studies. Another important difference is that the WSR-88DP simultaneously transmits horizontal (H) and vertical (V) polarized pulses. In contrast, S-Pol typically transmits alternating H and V pulses, which results in not only higher data quality for research but also allows for the cross-polar signal to be measured. The cross-polar signal provides estimates of the linear depolarization ratio (LDR) and the co- to cross-correlation coefficient that give additional microphysical information. This paper presents plots and interpretations of high-quality, high-resolution polarimetric data that demonstrate the value of S-Pol’s polarimetric measurements for atmospheric research.

Canadian Approaches to Optimizing Quality of Administrative Data for Health System Use, Research, and Linkage

Theme: Data and Linkage QualityObjectives:
 
 To define health data quality from clinical, data science, and health system perspectives
 To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data.
 To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives)
 To highlight how data linkage can be used to provide new insights into the quality of original data sources
 To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials
 
 Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards.
 Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture.
 Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11.
 Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality.
 Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data.
 Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost.
 This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use.
 CIHI content will include:
 
 Methods for optimizing the value of clinical-administrative data
 CIHI Information Quality Framework
 Reabstraction studies (e.g. physician documentation/coders’ experiences)
 Linkage analytics for data quality
 
 University of Calgary content will include:
 
 Defining/measuring health data quality
 Automated methods for quality assessment and improvement
 ICD-11 features and coding practices
 Electronic health record initiatives

Data Quality Automation: a Generic Approach for Large Linked Research Datasets

IntroductionWhen datasets are collected mainly for administrative rather than research purposes, data quality checks are necessary to ensure robust findings and to avoid biased results due to incomplete or inaccurate data.
 When done manually, data quality checks are time-consuming. We introduced automation to speed up the process and save effort.
 Objectives and ApproachWe have devised a set of automated generic quality checks and reporting, which can be run on any dataset in a relational database without any dataset-specific knowledge or configuration.
 The code is written in Python. Checks include: linkage quality, agreement with a population data source, comparison with previous data version, duplication checks, null count, value distribution and range, etc.
 Where dataset metadata is available, checks for validity against lookup tables are included, and the output report includes documentation on data contents. An HTML report with dynamic datatables and interactive graphs, allowing easy exploration of the results, is produced using RMarkdown.
 ResultsThe automation of the generic data quality check provides an easy and quick tool to report on data issues with minimal effort. It allows comparison with reference tables, lookups and previous versions of the same table to highlight differences. Moreover, this tool can be provided for researchers as a means to get more detailed understanding about their data.
 While other research data quality tools exist, this tool is distinguished by its features specific to linked data research, as well as implementation in a relational database environment. It has been successfully tested on datasets of over two billion rows.
 The tool was designed for use within the SAIL Databank, but could easily be adapted and used in other settings.
 Conclusion/ImplicationsThe effort spent on automating generic testing and reporting on data quality of research datasets is more than compensated by its outputs. Benefits include quick detection and scrutiny of many sources of invalid and incomplete data. This process can easily be expanded to accommodate more standard tests.

Master protocols in lung cancer: experience from Lung Master Protocol.

Contemporary advances in the understanding of the molecular and immunologic basis of metastatic lung cancer have firmly changed its treatment paradigm to a personalized, biomarker-driven approach. However, the majority of lung-cancer patients [especially lung squamous cell carcinoma (LUSC)] still do not have effective targeted therapeutic options. Master protocols, such as Lung-MAP, represent an innovative clinical trial approach designed to accelerate evaluation of novel biomarker-driven therapies. Lung-MAP is an umbrella trial for advanced LUSC and has been active since 2014. Cumulative experience from this overarching, multi-institution master protocol has demonstrated that centralized, real-time biomarker screening is feasible and substudy modularity is essential for protocol adaptability in a rapidly changing treatment landscape. In addition, screening and efficacy results from Lung-MAP affirm that LUSC has several putative drivers but remains difficult to effectively treat with targeted therapy. Master protocols are a feasible and efficient approach for evaluating biomarker-driven therapies in lung cancer. As we begin to target less common genomic and immunotherapy subtypes, centrally coordinated clinical trial designs such as Lung-MAP are necessary to rapidly deliver effective therapies to patients, whereas also maximizing the quality of research data obtained.

A Stepwise Procedure to Define a Data Collection Framework for a Clinical Biobank.

Current guidelines for clinical biobanking have a strong focus on obtaining, handling, and storage of biospecimens. However, to allow for research tying biomarker analysis to clinical decision making, there should be more focus on collection of data on donor characteristics. Therefore, our aim was to develop a stepwise procedure to define a framework as a tool to help start the data collection process in clinical biobanking. The Radboud Biobank (RB) is a central clinical biobanking facility designed in accordance with the standards set by the Parelsnoer Institute, a Dutch national biobank originally initiated with eight different disease cohorts. To organize the information of these cohorts, we used our experience and knowledge in the field of biobanking and translational research to identify research domains and information categories to classify data. We extended this classification system to a stepwise procedure for defining a data collection framework and examined its utility for existing RB biobanks. Our approach resulted in the definition of a three-step procedure: (1) Identification of research domains and relevant questions within the field that may benefit from biobank samples. (2) Identification of information categories and accompanying subcategories that are relevant for answering questions in identified research domains. (3) Reduction to an efficient framework based on essentiality and quality criteria. We showed the utility of the procedure for three existing RB biobanks. We developed guidelines for the definition of a framework that supports the standardization of the biobank data collection process. Connecting the biobank database to pertinent information collected from the electronic health record will improve data quality and efficiency for both care and research. This is crucial when using the corresponding biospecimens for scientific research. Further, it also facilitates the combination of different clinical biobanks for a specific disease.

Health of health care workers in Canadian nursing homes and pediatric hospitals: a cross-sectional study.

Poor health of health care workers affects quality of care, but research and health data for health care workers are scarce. Our aim was to compare physical/mental health among health care worker groups 1) within nursing homes and pediatric hospitals, 2) between the 2 settings and 3) with the physical/mental health of the Canadian population. Using cross-sectional data collected as part of the Translating Research in Elder Care program and the Translating Research on Pain in Children program, we examined the health of health care workers. In nursing homes, 169 registered nurses, 139 licensed practical nurses, 1506 care aides, 145 allied health care providers and 69 managers were surveyed. In pediatric hospitals, 63 physicians, 747 registered nurses, 155 allied health care providers, 49 nurse educators and 22 managers were surveyed. After standardization of the data for age and sex, we applied analyses of variance and general linear models, adjusted for multiple testing. Nursing home workers and registered nurses in pediatric hospitals had poorer mental health than the Canadian population. Scores were lowest for registered nurses in nursing homes (mean difference -4.4 [95% confidence interval -6.6 to -2.6]). Physicians in pediatric hospitals and allied health care providers in nursing homes had better physical health than the general population. We also found important differences in physical/mental health for care provider groups within and between care settings. Mental health is especially poor among nursing home workers, who care for a highly vulnerable and medically complex population of older adults. Strategies including optimized work environments are needed to improve the physical and mental health of health care workers to ameliorate quality of patient care.

Assessing the quality of administrative data for research: a framework from the Manitoba Centre for Health Policy.

The growth of administrative data repositories worldwide has spurred the development and application of data quality frameworks to ensure that research analyses based on these data can be used to draw meaningful conclusions. However, the research literature on administrative data quality is sparse, and there is little consensus regarding which dimensions of data quality should be measured. Here we present the core dimensions of the data quality framework developed at the Manitoba Centre for Health Policy, a world leader in the use of administrative data for research purposes, and provide examples and context for the application of these dimensions to conducting data quality evaluations. In sharing this framework, our ultimate aim is to promote best practices in rigorous data quality assessment among users of administrative data for research.

Improving quality of science through better animal welfare: the NC3Rs strategy.

Good animal welfare is linked to the quality of research data derived from laboratory animals, their validity as models of human disease, the number of animals required to reach statistical significance and the reproducibility of in vivo studies. Identifying new ways of understanding and improving animal welfare, and promoting these in the scientific community, is therefore a key part of the work of the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs). Our strategy for animal welfare includes funding research to generate an evidence base to support refinements, office-led data sharing to challenge existing practices, events and networks to raise awareness of the evidence base, and the creation of online and other resources to support practical implementation of refinement opportunities.

Crash data quality for road safety research: Current state and future directions

Crash databases are one of the primary data sources for road safety research. Therefore, their quality is fundamental for the accuracy of crash analyses and, consequently the design of effective countermeasures. Although crash data often suffer from correctness and completeness issues, these are rarely discussed or addressed in crash analyses. Crash reports aim to answer the five “W” questions (i.e. When?, Where?, What?, Who? and Why?) of each crash by including a range of attributes. This paper reviews current literature on the state of crash data quality for each of these questions separately. The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users’ demographics and inaccurate identification of crash contributory factors. It is shown that the extent and the severity of data quality issues are not equal between attributes and the level of impact in road safety analyses is not yet entirely known. This paper highlights areas that require further research and provides some suggestions for the development of intelligent crash reporting systems.