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Database : the Journal of Biological... Oct 2023The European Union Data Collection Framework (DCF) states that scientific data-driven assessments are essential to achieve sustainable fisheries. To respond to the DCF...
The European Union Data Collection Framework (DCF) states that scientific data-driven assessments are essential to achieve sustainable fisheries. To respond to the DCF call, this study introduces the information systems developed and used by Institut Català de Recerca per a la Governança del Mar (ICATMAR), the Catalan Institute of Research for the Governance of the Seas. The information systems include data from a biological monitoring, curation, processing, analysis, publication and web visualization for bottom trawl fisheries. Over the 4 years of collected data (2019-2022), the sampling program developed a dataset of over 1.1 million sampled individuals accounting for 24.6 tons of catch. The sampling data are ingested into a database through a data input website ensuring data management control and quality. The standardized metrics are automatically calculated and the data are published in the web visualizer, combined with fishing landings and Vessel Monitoring System (VMS) records. As the combination of remote sensing data with fisheries monitoring offers new approaches for ecosystem assessment, the collected fisheries data are also visualized in combination with georeferenced seabed habitats from the European Marine Observation and Data Network (EMODnet), climate and sea conditions from Copernicus Monitoring Environment Marine Service (CMEMS) on the web browser. Three public web-based products have been developed in the visualizer: geolocated bottom trawl samplings, biomass distribution per port or season and length-frequency charts per species. These information systems aim to fulfil the gaps in the scientific community, administration and civil society to access high-quality data for fisheries management, following the Findable, Accessible, Interoperable, Reusable (FAIR) principles, enabling scientific knowledge transfer. Database URL https://icatmar.github.io/VISAP/(www.icatmar.cat).
Topics: Humans; Animals; Ecosystem; Fisheries; Data Management; Data Collection; Web Browser; Fishes
PubMed: 37864836
DOI: 10.1093/database/baad067 -
Journal of Pain and Symptom Management Jul 2022Prospective cohort studies of individuals with serious illness and their family members, such as children receiving palliative care and their parents, pose challenges... (Review)
Review
CONTEXT
Prospective cohort studies of individuals with serious illness and their family members, such as children receiving palliative care and their parents, pose challenges regarding data management.
OBJECTIVE
To describe the design and lessons learned regarding the data management system for the Pediatric Palliative Care Research Network's Shared Data and Research (SHARE) project, a multicenter prospective cohort study of children receiving pediatric palliative care (PPC) and their parents, and to describe important attributes of this system, with specific considerations for the design of future studies.
METHODS
The SHARE study consists of 643 PPC patients and up to two of their parents who enrolled from April 2017 to December 2020 at seven children's hospitals across the United States. Data regarding demographics, patient symptoms, goals of care, and other characteristics were collected directly from parents or patients at 6 timepoints over a 24-month follow-up period and stored electronically in a centralized location. Using medical record numbers, primary collected data was linked to administrative hospitalization data containing diagnostic and procedure codes and other data elements. Important attributes of the data infrastructure include linkage of primary and administrative data; centralized availability of multilingual questionnaires; electronic data collection and storage system; time-stamping of instrument completion; and a separate but connected study administrative database used to track enrollment.
CONCLUSIONS
Investigators planning future multicenter prospective cohort studies can consider attributes of the data infrastructure we describe when designing their data management system.
Topics: Child; Cohort Studies; Data Management; Humans; Multicenter Studies as Topic; Palliative Care; Prospective Studies; Surveys and Questionnaires; United States
PubMed: 35339611
DOI: 10.1016/j.jpainsymman.2022.03.006 -
International Journal of Population... 2021Data pooling from pre-existing datasets can be useful to increase study sample size and statistical power in order to answer a research question. However, individual...
Data pooling from pre-existing datasets can be useful to increase study sample size and statistical power in order to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies: All Our Families; and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were harmonized or processed under a common format across the datasets considering the frequency of measurement, the timing of measurement, the measurement scale used, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies, which permit researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.
Topics: Alberta; Cohort Studies; Data Collection; Data Management; Female; Humans; Pregnancy; Sample Size
PubMed: 34888420
DOI: 10.23889/ijpds.v6i1.1680 -
Trials Mar 2022Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in...
BACKGROUND
Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran.
METHOD
This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used.
RESULTS
The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes.
CONCLUSION
According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.
Topics: Data Management; Diabetes Mellitus; Humans; Iran; Qualitative Research; Research Personnel
PubMed: 35241149
DOI: 10.1186/s13063-022-06110-5 -
F1000Research 2022: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR...
: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. : An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. : The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. : The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.
Topics: Humans; Data Management; Metadata; Information Dissemination; Surveys and Questionnaires; Workflow
PubMed: 36405555
DOI: 10.12688/f1000research.121714.2 -
American Journal of Biological... Nov 2022Previous research has shown that while missing data are common in bioarchaeological studies, they are seldom handled using statistically rigorous methods. The primary...
OBJECTIVES
Previous research has shown that while missing data are common in bioarchaeological studies, they are seldom handled using statistically rigorous methods. The primary objective of this article is to evaluate the ability of imputation to manage missing data and encourage the use of advanced statistical methods in bioarchaeology and paleopathology. An overview of missing data management in biological anthropology is provided, followed by a test of imputation and deletion methods for handling missing data.
MATERIALS AND METHODS
Missing data were simulated on complete datasets of ordinal (n = 287) and continuous (n = 369) bioarchaeological data. Missing values were imputed using five imputation methods (mean, predictive mean matching, random forest, expectation maximization, and stochastic regression) and the success of each at obtaining the parameters of the original dataset compared with pairwise and listwise deletion.
RESULTS
In all instances, listwise deletion was least successful at approximating the original parameters. Imputation of continuous data was more effective than ordinal data. Overall, no one method performed best and the amount of missing data proved a stronger predictor of imputation success.
DISCUSSION
These findings support the use of imputation methods over deletion for handling missing bioarchaeological and paleopathology data, especially when the data are continuous. Whereas deletion methods reduce sample size, imputation maintains sample size, improving statistical power and preventing bias from being introduced into the dataset.
Topics: Archaeology; Sample Size; Research Design; Data Management; Bias
PubMed: 36790608
DOI: 10.1002/ajpa.24614 -
BMJ Open Aug 2022This article aims to measure the willingness of the Swiss public to participate in personalised health research, and their preferences regarding data management and...
OBJECTIVES
This article aims to measure the willingness of the Swiss public to participate in personalised health research, and their preferences regarding data management and governance.
SETTING
Results are presented from a nationwide survey of members of the Swiss public.
PARTICIPANTS
15 106 randomly selected Swiss residents received the survey in September 2019. The response rate was 34.1% (n=5156). Respondent age ranged from 18 to 79 years, with fairly uniform spread across sex and age categories between 25 and 64 years.
PRIMARY AND SECONDARY OUTCOME MEASURES
Willingness to participate in personalised health research and opinions regarding data management and governance.
RESULTS
Most respondents preferred to be contacted and reconsented for each new project using their data (39%, 95% CI: 37.4% to 40.7%), or stated that their preference depends on the project type (29.4%, 95% CI: 27.9% to 31%). Additionally, a majority (52%, 95% CI: 50.3% to 53.8%) preferred their data or samples be stored anonymously or in coded form (43.4%, 95% CI: 41.7% to 45.1%). Of those who preferred that their data be anonymised, most also indicated a wish to be recontacted for each new project (36.8%, 95% CI: 34.5% to 39.2%); however, these preferences are in conflict. Most respondents desired to personally own their data. Finally, most Swiss respondents trust their doctors, along with researchers at universities, to protect their data.
CONCLUSION
Insight into public preference can enable Swiss biobanks and research institutions to create management and governance strategies that match the expectations and preferences of potential participants. Models allowing participants to choose how to interact with the process, while more complex, may increase individual willingness to provide data to biobanks.
Topics: Adolescent; Adult; Aged; Biological Specimen Banks; Data Management; Humans; Middle Aged; Surveys and Questionnaires; Switzerland; Trust; Young Adult
PubMed: 36028266
DOI: 10.1136/bmjopen-2022-060844 -
Therapeutic Innovation & Regulatory... Sep 2021The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management...
BACKGROUND
The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity.
METHODS
Tufts Center for the Study of Drug Development (Tufts CSDD)-in collaboration with IBM Watson Health-conducted an online global survey between September and October 2020.
RESULTS
One hundred ninety four verified responses were analyzed. Planned and unplanned mid-study updates were the top challenges mentioned and their management was time intensive. Respondents reported an average of 4.1 planned and 3.7 unplanned mid-study updates per clinical trial.
CONCLUSION
Mid-study database updates are disruptive and present a major opportunity to accelerate cycle times and improve efficiency, particularly as protocol designs become more flexible and the diversity of data, most notably unstructured data, increases.
Topics: Data Management; Drug Development; Humans; Pain; Surveys and Questionnaires
PubMed: 33963525
DOI: 10.1007/s43441-021-00301-z -
Computer Methods and Programs in... Nov 2021In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these...
BACKGROUND AND OBJECTIVES
In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are currently not able to cover the needs of adaptation to the continuous changes in the practice of the trials due to the heterogeneous and dynamic nature of the clinical research data. These systems are usually proprietary solutions provided by vendors for specific tasks. In this work, we propose FIMED, a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials.
METHODS
This tool allows a dynamic and incremental design of patients' profiles in the context of clinical trials, providing a flexible user interface that hides the complexity of using databases. Clinical researchers will be able to define personalized data schemas according to their needs and clinical study specifications. Thus, FIMED allows the incorporation of separate clinical data analysis from multiple trials.
RESULTS
The efficiency of the software has been demonstrated by a real-world use case for a clinical assay in Melanoma disease, which has been indeed anonymized to provide a user demonstration. FIMED currently provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data: heatmap visualization, clusterheatmap visualization, as well as gene regulatory network inference and visualization. An instance of this tool is freely available on the web at https://khaos.uma.es/fimed. It can be accessed with a demo user account, "researcher", using the password "demo".
CONCLUSION
This paper shows FIMED as a flexible and user-friendly way of managing multidimensional clinical research data. Hence, without loss of generality, FIMED is flexible enough to be used in the context of any other disease where clinical data and assays are involved.
Topics: Data Management; Databases, Factual; Gene Regulatory Networks; Humans; Internet; Software; User-Computer Interface
PubMed: 34740063
DOI: 10.1016/j.cmpb.2021.106496 -
Journal of Chemical Information and... Jan 2022Projects in chemo- and bioinformatics often consist of scattered data in various types and are difficult to access in a meaningful way for efficient data analysis. Data...
Projects in chemo- and bioinformatics often consist of scattered data in various types and are difficult to access in a meaningful way for efficient data analysis. Data is usually too diverse to be even manipulated effectively. Sdfconf is data manipulation and analysis software to address this problem in a logical and robust manner. Other software commonly used for such tasks are either not designed with molecular and/or conformational data in mind or provide only a narrow set of tasks to be accomplished. Furthermore, many tools are only available within commercial software packages. Sdfconf is a flexible, robust, and free-of-charge tool for linking data from various sources for meaningful and efficient manipulation and analysis of molecule data sets. Sdfconf packages molecular structures and metadata into a complete ensemble, from which one can access both the whole data set and individual molecules and/or conformations. In this software note, we offer some practical examples of the utilization of sdfconf.
Topics: Computational Biology; Data Analysis; Data Management; Software
PubMed: 34932340
DOI: 10.1021/acs.jcim.1c01051