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The Journal of Law, Medicine & Ethics :... Dec 2020The firearms data infrastructure in the United States is severely limited in scope and fragmented in nature. Improved data systems are needed in order to address gun...
The firearms data infrastructure in the United States is severely limited in scope and fragmented in nature. Improved data systems are needed in order to address gun violence and promote productive conversation about gun policy. In the absence of federal leadership in firearms data systems improvement, motivated states may take proactive steps to stitch gaps in data systems. We propose that states evaluate the gaps in their systems, expand data collection, and improve data presentation and availability.
Topics: Data Collection; Data Systems; Databases as Topic; Federal Government; Firearms; Gun Violence; History, 20th Century; Humans; Information Systems; State Government; United States
PubMed: 33404295
DOI: 10.1177/1073110520979399 -
Methods in Molecular Biology (Clifton,... 2021Recently, digitization of biomedical processes has accelerated, in no small part due to the use of machine learning techniques which require large amounts of labeled...
Recently, digitization of biomedical processes has accelerated, in no small part due to the use of machine learning techniques which require large amounts of labeled data. This chapter focuses on the prerequisite steps to the training of any algorithm: data collection and labeling. In particular, we tackle how data collection can be set up with scalability and security to avoid costly and delaying bottlenecks. Unprecedented amounts of data are now available to companies and academics, but digital tools in the biomedical field encounter a problem of scale, since high-throughput workflows such as high content imaging and sequencing can create several terabytes per day. Consequently data transport, aggregation, and processing is challenging.A second challenge is maintenance of data security. Biomedical data can be personally identifiable, may constitute important trade-secrets, and be expensive to produce. Furthermore, human biomedical data is often immutable, as is the case with genetic information. These factors make securing this type of data imperative and urgent. Here we address best practices to achieve security, with a focus on practicality and scalability. We also address the challenge of obtaining usable, rich metadata from the collected data, which is a major challenge in the biomedical field because of the use of fragmented and proprietary formats. We detail tools and strategies for extracting metadata from biomedical scientific file formats and how this underutilized metadata plays a key role in creating labeled data for use in the training of neural networks.
Topics: Algorithms; Biomedical Research; Computer Security; Data Collection; Machine Learning; Neural Networks, Computer; Workflow
PubMed: 32804374
DOI: 10.1007/978-1-0716-0826-5_16 -
The Lancet. Neurology Nov 2019
Topics: Congresses as Topic; Data Collection; Decision Making, Shared; Humans; International Cooperation; Multiple Sclerosis; Patient Reported Outcome Measures; Quality of Life; Research; Societies, Medical; Societies, Scientific
PubMed: 31609205
DOI: 10.1016/S1474-4422(19)30357-6 -
BMC Pregnancy and Childbirth Mar 2021Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data... (Observational Study)
Observational Study
BACKGROUND
Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data (E-data) collection needs to account for these challenges. The Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) was an observational study to assess measurement of indicators for priority maternal and newborn interventions and took place in five hospitals in Bangladesh, Nepal and Tanzania (July 2017-July 2018). E-data tools were required to capture individually-linked, timed observation of care, data extraction from hospital register-records or case-notes, and exit-survey data from women.
METHODS
To evaluate this process for EN-BIRTH, we employed a framework organised around five steps for E-data design, data collection and implementation. Using this framework, a mixed methods evaluation synthesised evidence from study documentation, standard operating procedures, stakeholder meetings and design workshops. We undertook focus group discussions with EN-BIRTH researchers to explore experiences from the three different country teams (November-December 2019). Results were organised according to the five a priori steps.
RESULTS
In accordance with the five-step framework, we found: 1) Selection of data collection approach and software: user-centred design principles were applied to meet the challenges for observation of rapid, concurrent events around the time of birth with time-stamping. 2) Design of data collection tools and programming: required extensive pilot testing of tools to be user-focused and to include in-built error messages and data quality alerts. 3) Recruitment and training of data collectors: standardised with an interactive training package including pre/post-course assessment. 4) Data collection, quality assurance, and management: real-time quality assessments with a tracking dashboard and double observation/data extraction for a 5% case subset, were incorporated as part of quality assurance. Internet-based synchronisation during data collection posed intermittent challenges. 5) Data management, cleaning and analysis: E-data collection was perceived to improve data quality and reduce time cleaning.
CONCLUSIONS
The E-Data system, custom-built for EN-BIRTH, was valued by the site teams, particularly for time-stamped clinical observation of complex multiple simultaneous events at birth, without which the study objectives could not have been met. However before selection of a custom-built E-data tool, the development time, higher training and IT support needs, and connectivity challenges need to be considered against the proposed study or programme's purpose, and currently available E-data tool options.
Topics: Bangladesh; Data Accuracy; Electronic Health Records; Female; Focus Groups; Hospital Information Systems; Hospitals; Humans; Infant, Newborn; Nepal; Perinatal Care; Pregnancy; Software; Surveys and Questionnaires; Tanzania
PubMed: 33765951
DOI: 10.1186/s12884-020-03426-5 -
The Veterinary Clinics of North... May 2020Data collection and research about adverse effects associated with euthanasia are lacking in the veterinary profession. The goal of this article is to review current... (Review)
Review
Data collection and research about adverse effects associated with euthanasia are lacking in the veterinary profession. The goal of this article is to review current research about euthanasia and propose concepts to collect and document euthanasia data to support future studies. A better understanding of the side effects witnessed near perimortem should provide benefits to pet owners, veterinarians, and staff, especially if methods are uncovered to minimize or mitigate the adverse events witnessed. Such data can provide valuable insight and guidance in improving the quality of death and furthering education about the dying process.
Topics: Animal Welfare; Animals; Data Collection; Euthanasia, Animal; Pets
PubMed: 32115279
DOI: 10.1016/j.cvsm.2019.12.006 -
Current Environmental Health Reports Dec 2019Neighborhood disorder has received attention as a determinant of health in urban contexts, through pathways that include psychosocial stress, perceived safety, and... (Review)
Review
PURPOSE OF REVIEW
Neighborhood disorder has received attention as a determinant of health in urban contexts, through pathways that include psychosocial stress, perceived safety, and physical activity. This review provides a summary of data collection methods, descriptive terms, and specific items employed to assess neighborhood disorder/order.
RECENT FINDINGS
The proliferation of methods and terminology employed in measuring neighborhood disorder (or neighborhood order) noted over the past two decades has made related studies increasingly difficult to compare. Following a search of peer-reviewed articles published from January 1998 to May 2018, this rapid literature review identified 18 studies that described neighborhood environments, yielding 23 broad terms related to neighborhood disorder/order, and a total of 74 distinct measurable items. A majority of neighborhood disorder/order measurements were assessed using primary data collection, often relying on resident self-report or investigatory observations conducted in person or using stored images for virtual audits. Items were balanced across signs of order or disorder, and further classification was proposed based on whether items were physically observable and relatively stable over time.
Topics: Data Collection; Environmental Health; Humans; Residence Characteristics
PubMed: 31773497
DOI: 10.1007/s40572-019-00259-z -
Social Studies of Science Jun 2024Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and...
Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and to which data flow. Drawing upon ethnographic fieldwork in two connected data-intensive laboratories and the concept of domestication, we explore what it takes to bring data 'home' into the laboratory. As data come and dwell in the home, they are made to follow rituals, and as a result, data are reshaped and form ties with the laboratory and its practitioners. We identify four main ways of domesticating data. First, through about the data's origins, data practitioners draw the boundaries of their laboratory. Second, through , staff transform samples into digital data that can travel well while ruling what data can be let into the home. Third, through , data practitioners become familiar with their data and at the same time imprint the data, thus making them belong to their home. Finally, through , staff turn data into a resource for knowledge production. Through the lens of domestication, we see the data economy as a collection of homes connected by flows, and it is because data are tamed and attached to homes that they become valuable knowledge tools. Such domestication practices also have broad implications for staff, who in the process of 'homing' data, come to belong to the laboratory. To conclude, we reflect on what these domestication processes-which silence unusual behaviours in the data-mean for the knowledge produced in data-intensive research.
Topics: Anthropology, Cultural; Data Collection
PubMed: 38006306
DOI: 10.1177/03063127231212506 -
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 -
American Journal of Primatology Jul 2020In social species, network centralities of group members shape social transmission and other social phenomena. Different factors have been found to influence the...
In social species, network centralities of group members shape social transmission and other social phenomena. Different factors have been found to influence the measurement of social networks, such as data collection and observation methods. In this study, we collected data on adults and juveniles and examined the effect of data collection method (ad libitum sampling vs. focal animal sampling) and observation method (interaction-grooming; play-vs. association-arm-length; 2 m; 5 m proximities-) on social networks in wild vervet monkeys. First, we showed using a bootstrapping method, that uncertainty of ad libitum grooming and play matrices were lesser than uncertainty of focal matrices. Nevertheless, grooming and play networks constructed from ad libitum and focal animal sampling were very similar and highly correlated. We improved the certainty of both grooming and play networks by pooling focal and ad libitum matrices. Second, we reported a high correlation between the proximity arm-length network and the focal grooming one making an arm-length proximity network a reasonable proxy for a grooming one in vervet monkeys. However, we did not find such a correlation between proximity networks and the play one. Studying the effects of methodological issues as data collection and observation methods can help improve understanding of what shapes social networks and which data collection method to choose to study sociality.
Topics: Animals; Behavior Observation Techniques; Behavior, Animal; Chlorocebus aethiops; Data Collection; Female; Grooming; Male; Play and Playthings; Social Behavior
PubMed: 32310316
DOI: 10.1002/ajp.23137