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Future Directions for the Toolbox Dialogue Initiative
Published in Graham Hubbs, Michael O’Rourke, Steven Hecht Orzack, The Toolbox Dialogue Initiative, 2020
Sanford D. Eigenbrode, Stephanie E. Vasko, Anna Malavisi, Bethany K. Laursen, Michael O’Rourke
TDI has created a living data management plan to manage the large volume of data that has been collected over the years. It defines what files should include, how files should be named, where files should be stored based on their contents, acceptable file formats, what items from each workshop should be shared online, and how the log of workshops should be updated. The initial plan was created by a team that included MSU members of the TDI community using DMPTool (University of California Curation Center 2019). TDI now requires that one of its community members serve as the TDI data manager. One of their tasks is to periodically update the data management plan, making it flexible enough to adapt to changes in staff, tools, and research protocols. Developing a clear and dynamic data management strategy is especially important for TDI because grant agencies increasingly require data sharing, open data, and a data management plan.
Development and Supervision of Independent Projects
Published in Walter Fox Smith, Experimental Physics, 2020
Your work plan should also lay out your proposed approach for collecting and analyzing data. Clearly identify the quantities that you are able to measure. Then consider the physical models that you want to test. What must you do with the quantities you measure in order to compare them with the models that you are exploring? How will you handle outliers? How will decide whether your data is appropriate for your goals or whether you will need to iterate the design of your apparatus or your experimental methods? Thinking ahead about your approach to analysis helps avoid subconscious bias, including confirmation bias. In addition, you should decide on a data management plan: where will you store your data, what file naming conventions will you use, how will you keep track of raw versus processed data, and who has access to and responsibility for the data?
Ethical Considerations in Engineering Writing
Published in Edward J. Rothwell, Michael J. Cloud, Engineering Writing by Design, 2020
Edward J. Rothwell, Michael J. Cloud
Contractual language may also list rules for data retention and ownership. Storing and archiving sensitive data may require special procedures (including encryption) and safe spaces (such as locked vaults). For example, certain federal contracts may include ITAR (International Traffic in Arms) clauses restricting data access by non-citizens. Other federal agencies (such as the National Science Foundation) may require specific data management plans describing the storage, retention, and dissemination of research data.
Data Stream Management for CPS-based Healthcare: A Contemporary Review
Published in IETE Technical Review, 2022
Sadhana Tiwari, Sonali Agarwal
Data Management Plan Guidelines [61–63] for stream processing includes streaming nature, type, scope and range of the high-speed sequence of infinite data streams. The data management plan emphasizes the following aspects of healthcare streaming data The description of data type, sample size, data acquisition software.Format of data and metadata, standards as per healthcare application.Design policies for privacy protection, confidentiality, security and other rights or requirements.Develop methods for re-use, re-distribution and production of derivatives.
Co-creation in support of responsible research and innovation: an analysis of three stakeholder workshops on nanotechnology for health
Published in Journal of Responsible Innovation, 2022
Sikke R. Jansma, Anne M. Dijkstra, Menno D.T. de Jong
The data management plan represents a number of values and needs that were defined earlier by the citizens. The various stakeholders were all able to contribute to the suggestions for the data management plan, and the results of the questionnaire indicated that the stakeholders were positive about the process of the workshop. Furthermore, the outcome was regarded by the stakeholders as relevant and feasible. However, in the evaluation interview, the business developer of the company working on the artificial pancreas stated that he would not (yet) implement the suggestion in their product. Although he acknowledged the relevance of data management and learned from the workshop that it would be good to integrate a functionality to personalize the data sharing, the company had other priorities.
Chlorine exposure during a biological decontamination study in a mock subway tunnel
Published in Journal of Occupational and Environmental Hygiene, 2019
John D. Archer, Rebecca DeVries, Andrew J. Imler
Monitoring of personal exposure to chlorine during surface decontamination, mixing of pAB solution, and immersion of decon waste was conducted. Chlorine monitors were affixed to breathing zone heights of the workers who entered the Exclusion Zone to perform the spray decontamination of the track, ballast (rocks), platform, walls, and ceiling of the mock subway tunnel. Exposure times ranged from 12–41 min in duration. Due to the risk of splashing and spattering from the decontaminant spray when spraying overhead, the monitors were moved from the front lapel to the workers’ back left shoulder area (Figure1) but were still maintained at breathing zone height. This positioning of the monitors allowed some protection from direct overspray. Monitors were programmed to log chlorine concentrations at 5-sec intervals for the duration of the decontamination periods. Sensor calibrations were conducted prior to use each day as well as post-use following manufacturer instructions using a chlorine gas calibration cylinder (10 ppm). Data backups were performed daily in accordance with the onsite data management plan.