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Methods in Molecular Biology (Clifton,... 2021Cryo electron microscopy (cryo-EM) has become a method of choice in structural biology to analyze isolated complexes and cellular structures. This implies adequate...
Cryo electron microscopy (cryo-EM) has become a method of choice in structural biology to analyze isolated complexes and cellular structures. This implies adequate imaging of the specimen and advanced image-processing methods to obtain high-resolution 3D reconstructions. The use of a Volta phase plate in cryo-EM drastically increases the image contrast while being able to record images at high acceleration voltage and close to focus, i.e., at conditions where high-resolution information is best preserved. During image processing, higher contrast images can be aligned and classified better than lower quality ones resulting in increased data quality and the need for less data. Here, we give step-by-step guidelines on how to set up high-quality VPP cryo-EM single particle data collections, as exemplified by human ribosome data acquired during a one-day data collection session. Further, we describe specific technical details in image processing that differ from conventional single particle cryo-EM data analysis.
Topics: Cryoelectron Microscopy; Data Accuracy; Data Collection; Humans; Imaging, Three-Dimensional; Molecular Biology; Ribosomes; Single Molecule Imaging
PubMed: 33950395
DOI: 10.1007/978-1-0716-1406-8_14 -
Global Health, Science and Practice Sep 2022An objective of the Information Revolution Roadmap of Ethiopia's Health Sector Transformation Plan was to improve health management information system (HMIS) data...
INTRODUCTION
An objective of the Information Revolution Roadmap of Ethiopia's Health Sector Transformation Plan was to improve health management information system (HMIS) data quality and data use at the point of health service delivery. We aimed to assess drivers of and barriers to improving HMIS data quality and use, focusing on key Information Revolution strategies including Connected Woreda, capacity building, performance monitoring teams, and motivational incentives.
METHODS
We conducted an interpretative qualitative study across all 11 health centers in 3 subcities of Addis Ababa, Ethiopia: Yeka, Akaki-Kaliti, and Ledeta. A total of 40 key informant interviews and 6 focus group discussions with a total of 43 discussants were conducted. We coded information gathered line-by-line and grouped responses under thematic codes as they emerged. Findings were triangulated and validated.
RESULTS
Our findings indicate that the main drivers of data quality and use at the point of service delivery were the use of the Connected Woreda strategy and its tools, capacity-building activities including mentorship, performance monitoring-team activities that led to active leadership engagement, and motivational incentives for data producers and users. Barriers to optimal data-use practices were the use of duplicative data collection tools at health facilities, under-developed health information system infrastructure, inadequate health information technician staffing and capacity limitations at the health facility level, insufficient leadership commitment, and unfavorable health worker attitudes toward data.
DISCUSSION
Improvements in quality and use of HMIS data at health facilities are expected to result in delivering better-quality health services to the community as data enable health workers to identify gaps in health care, fix them, and monitor improvements. Future investments should focus on strengthening the promising data-use practices, resolving bottlenecks caused by duplicative data collection tools, enhancing individual and institutional capacity, addressing suboptimal health worker attitudes toward data, and overcoming infrastructure and connectivity challenges.
Topics: Data Accuracy; Ethiopia; Focus Groups; Health Facilities; Humans; Qualitative Research
PubMed: 36109055
DOI: 10.9745/GHSP-D-21-00689 -
PloS One 2021Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to...
Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.
Topics: Archives; Bibliometrics; Data Collection; Data Management; Humans; Information Dissemination; Psychology; Software; Surveys and Questionnaires
PubMed: 34019600
DOI: 10.1371/journal.pone.0252047 -
Journal of Biomedical Informatics Jul 2020The value of robust and responsible data sharing in clinical research and healthcare is recognized by patients, patient advocacy groups, researchers, journal editors,...
The value of robust and responsible data sharing in clinical research and healthcare is recognized by patients, patient advocacy groups, researchers, journal editors, and the healthcare industry globally. Privacy and security concerns acknowledged, the act of exchanging data (interoperability) along with its meaning (semantic interoperability) across studies and between partners has been difficult, if not elusive. For shared data to retain its value, a recommendation has been made to follow the Findable, Accessible, Interoperable, Reusable (FAIR) principles. Without applying appropriate data exchange standards with domain-relevant content standards and accessible rich metadata that uses applicable terminologies, interoperability is burdened by the need for transformation and/or mapping. These obstacles to interoperability limit the findability, accessibility and reusability of data, thus diminishing its value and making it impossible to adhere to FAIR principles. One effort to standardize data collection has been through common data elements (CDEs). CDEs are data collection units comprising one or more questions together with a set of valid values. Some CDEs contain standardized terminology concepts that define the meaning of the data, and others include links to unique terminology concept identifiers and unique identifiers for each CDE; however, usually CDEs are defined for specific projects or collaborations and lack traceable or machine readable semantics. While the name implies that these are 'common', this has not necessarily been a requirement, and many CDEs have not been commonly used. The National Institutes of Health (NIH) CDEs are, in fact, a conglomerate of CDEs developed in silos by various NIH institutes. Therefore, CDEs have not brought the anticipated benefit to the industry through widescale interoperability, nor is there widespread reuse of CDEs. Certain institutes in the NIH recommend, albeit do not enforce, institute-specific preferred CDEs; however, at the NIH level a preponderance of choice and a lack of any overarching harmonization of CDEs or consistency in linking them to controlled terminology or common identifiers create confusion for researchers in their efforts to identify the best CDEs for their protocol. The problem of comparing data among studies is exacerbated when researchers select different CDEs for the same variable or data collection field. This manuscript explores reasons for the disappointingly low adoption of CDEs and the inability of CDEs or other clinical research standards to broadly solve the interoperability and data sharing problems. Recommendations are offered for rectifying this situation to enable responsible data sharing that will help in adherence to FAIR principles and the realization of Learning Health Systems for the sake of all of us as patients.
Topics: Biomedical Research; Common Data Elements; Humans; Information Dissemination; Metadata; Population Health
PubMed: 32407878
DOI: 10.1016/j.jbi.2020.103421 -
Yearbook of Medical Informatics Aug 2021To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations... (Review)
Review
OBJECTIVES
To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations that can address the identified gaps and challenges.
METHODS
A literature review of relevant peer-reviewed and grey literature published from January 2020 to December 2020 was conducted to inform the paper.
RESULTS
The literature revealed several themes regarding health information management and health informatics challenges and gaps: information systems and information technology infrastructure; data collection, quality, and standardization; and information governance and use. These challenges and gaps were often driven by public policy and funding constraints.
CONCLUSIONS
COVID-19 exposed complexities related to responding to a world-wide, fast moving, quickly spreading novel virus. Longstanding gaps and ongoing challenges in the local, national, and global health and public health information systems and data infrastructure must be addressed before we are faced with another global pandemic.
Topics: COVID-19; Data Accuracy; Data Collection; Humans; Information Management; Medical Informatics; Public Health Administration; Public Health Practice; United States
PubMed: 34479380
DOI: 10.1055/s-0041-1726499 -
Sensors (Basel, Switzerland) May 2023The fact that advanced technologies and their economic applications have generated increasing resource costs justifies the transition from a linear approach to a... (Review)
Review
The fact that advanced technologies and their economic applications have generated increasing resource costs justifies the transition from a linear approach to a circular one in order to control these costs. From this perspective, this study presents how artificial intelligence can help achieve this goal. Therefore, at the beginning of this article, we begin with an introduction and brief review of the literature on the subject. Our research procedure involved the combination of qualitative and quantitative forms of research using mixed methods. In this study, we presented and analyzed five chatbot solutions used in the field of the circular economy. The analysis of these five chatbots helped us design, in the second part of this paper, the procedures for data collection, training, development, and testing of a chatbot using various natural language processing (NLP) and deep processing (DP) techniques. Additionally, we include discussions and some conclusions regarding all aspects of the subject to see how they can help us in future studies. Furthermore, our future research with this topic will have as the goal the effective construction of a chatbot dedicated to the circular economy.
Topics: Artificial Intelligence; Data Collection; Natural Language Processing; Records; Software
PubMed: 37299717
DOI: 10.3390/s23114990 -
International Journal of Population... 2023Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a...
Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of myths and misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably, many of these myths and misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.
Topics: Humans; Data Accuracy; Data Collection; Databases, Factual; Information Storage and Retrieval; Medical Record Linkage; Population Health
PubMed: 37636835
DOI: 10.23889/ijpds.v8i1.2115 -
Addiction (Abingdon, England) Oct 2020Amazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1)...
AIMS
Amazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1) provides an introduction to the mechanics and validity of MTurk research; (2) gives examples of MTurk research; and (3) discusses current limitations and best practices in MTurk research.
METHODS
We review four use cases of MTurk for research relevant to addictions: (1) the development of novel measures, (2) testing interventions, (3) the collection of longitudinal use data to determine the feasibility of longer-term studies of substance use and (4) the completion of large batteries of assessments to characterize the relationships between measured constructs. We review concerns with the platform, ways of mitigating these and important information to include when presenting findings.
RESULTS
MTurk has proved to be a useful source of data for behavioral science more broadly, with specific applications to addiction science. However, it is still not appropriate for all use cases, such as population-level inference. To live up to the potential of highly transparent, reproducible science from MTurk, researchers should clearly report inclusion/exclusion criteria, data quality checks and reasons for excluding collected data, how and when data were collected and both targeted and actual participant compensation.
CONCLUSIONS
Although on-line survey research is not a substitute for random sampling or clinical recruitment, the Mechanical Turk community of both participants and researchers has developed multiple tools to promote data quality, fairness and rigor. Overall, Mechanical Turk has provided a useful source of convenience samples despite its limitations and has demonstrated utility in the engagement of relevant groups for addiction science.
Topics: Behavior, Addictive; Behavioral Research; Crowdsourcing; Data Accuracy; Data Collection; Humans; Patient Selection
PubMed: 32135574
DOI: 10.1111/add.15032 -
Bulletin of the World Health... Dec 2019
Topics: Community Health Services; Data Collection; Global Health; Health Policy; Healthy Aging; Humans; Life Style; Social Work; World Health Organization
PubMed: 31819284
DOI: 10.2471/BLT.19.246801 -
BMC Bioinformatics Jun 2022Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be...
BACKGROUND
Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring.
RESULTS
We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations.
CONCLUSION
Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.
Topics: Crops, Agricultural; Data Collection; Phenotype; Plant Breeding
PubMed: 35668357
DOI: 10.1186/s12859-022-04755-2