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GigaScience Dec 2022The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience...
The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.
Topics: Animals; Humans; Data Management; Neuroimaging; Research Personnel
PubMed: 37401720
DOI: 10.1093/gigascience/giad049 -
Journal of Healthcare Engineering 2021Nowadays, the adoption of Internet of Things (IoT) technology worldwide is accelerating the digital transformation of healthcare industry. In this context, smart...
Nowadays, the adoption of Internet of Things (IoT) technology worldwide is accelerating the digital transformation of healthcare industry. In this context, smart healthcare (s-healthcare) solutions are ensuring better and innovative opportunities for healthcare providers to improve patients' care. However, these solutions raise also new challenges in terms of security and privacy due to the diversity of stakeholders, the centralized data management, and the resulting lack of trustworthiness, accountability, and control. In this paper, we propose an end-to-end Blockchain-based and privacy-preserving framework called SmartMedChain for data sharing in s-healthcare environment. The Blockchain is built on Hyperledger Fabric and stores encrypted health data by using the InterPlanetary File System (IPFS), a distributed data storage solution with high resiliency and scalability. Indeed, compared to other propositions and based on the concept of smart contracts, our solution combines both data access control and data usage auditing measures for both Medical IoT data and Electronic Health Records (EHRs) generated by s-healthcare services. In addition, s-healthcare stakeholders can be held accountable by introducing an innovative Privacy Agreement Management scheme that monitors the execution of the service in respect of patient preferences and in accordance with relevant privacy laws. Security analysis and experimental results show that the proposed SmartMedChain is feasible and efficient for s-healthcare environments.
Topics: Blockchain; Data Management; Delivery of Health Care; Electronic Health Records; Humans; Privacy
PubMed: 34777733
DOI: 10.1155/2021/4145512 -
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 -
JCO Clinical Cancer Informatics Mar 2022
Topics: Clinical Trials as Topic; Data Management; Genomics; Humans
PubMed: 35404674
DOI: 10.1200/CCI.21.00193 -
Nucleic Acids Research Jan 2021The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) contains annotated isolate genome and metagenome datasets sequenced at the...
The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) contains annotated isolate genome and metagenome datasets sequenced at the DOE's Joint Genome Institute (JGI), submitted by external users, or imported from public sources such as NCBI. IMG v 6.0 includes advanced search functions and a new tool for statistical analysis of mixed sets of genomes and metagenome bins. The new IMG web user interface also has a new Help page with additional documentation and webinar tutorials to help users better understand how to use various IMG functions and tools for their research. New datasets have been processed with the prokaryotic annotation pipeline v.5, which includes extended protein family assignments.
Topics: Data Analysis; Data Management; Databases, Genetic; Genome, Archaeal; Genome, Microbial; Metagenome; RNA, Ribosomal, 16S; Search Engine
PubMed: 33119741
DOI: 10.1093/nar/gkaa939 -
Advances in Nutrition (Bethesda, Md.) Feb 2021In human nutrition randomized controlled trials (RCTs), planning, and careful execution of clinical data collection and management are vital for producing valid and...
In human nutrition randomized controlled trials (RCTs), planning, and careful execution of clinical data collection and management are vital for producing valid and reliable results. In this article, we provide an overview of best practices for biospecimen collection and analyses, and for the fundamentals of clinical data management, including preparation and study startup; data collection, entry, cleaning, and authentication; and database lock. The reader is also referred to additional resources for information to assist in the planning and conduct of human RCTs. The tools and strategies described are expected to improve the quality of data produced in human nutrition research that can, therefore, be used to support food and nutrition policies.
Topics: Data Management; Food; Humans; Laboratories; Nutritional Status; Randomized Controlled Trials as Topic
PubMed: 33200184
DOI: 10.1093/advances/nmaa088 -
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 -
Sensors (Basel, Switzerland) Oct 2021Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors,...
Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.
Topics: Big Data; Data Management; Databases, Factual; Prognosis; Reproducibility of Results
PubMed: 34696058
DOI: 10.3390/s21206841 -
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