As big data applications are expanding at a much faster pace, more and more businesses are choosing the path of digital transformation to maintain relevancy and stay abreast with the current trends. 2006. The goals of the study were to identify barriers to data curation, to recognize unmet researcher needs within the university environment, and to gain a holistic understanding of the workflow… Personal Practices and Training: It’s true that the ability to leverage data isn’t everybody’s forte and transforming it into information that is consistent, correct and comprehensive is often found to be missing. In this case, data rights can become a source of conflict, as the researcher’s institution asserts ownership over data produced by university-owned scanners. •   Data files: Excel, SPSS, STATA, ArcGIS, txt, various public data sets •    Are the data backed up? The volume, velocity, and variety of data that is being generated has overwhelmed the capabilities of infrastructure and analytics we have today. Universities’ common practice of limiting access to institutional networks to formally affiliated individuals has also contributed to this problem by making university-based systems of little use to multi-institutional collaborations. In the best-case scenario, a data specialist would be fully integrated into a research team and would also conduct research. The organization of digital files is also very difficult for this researcher, and she finds the file management tools that are part of a computer’s operating system insufficient for her needs. The program approach is the best fit for data governance in which one would be allowed to define a series of project streams that focus on one key area. Data silos. These data are vulnerable to loss when researchers upgrade their computers or software, and few researchers put more than minimal effort into organizing non-active data or ensuring its continued compatibility with new software or hardware. Although this project has both an NSF data management plan and a physical anthropology data-sharing plan (a standard in physical anthropology for a number of years), several factors limit the effective reuse of the project’s research data. Even individuals who are early in their research career may have amassed significant bodies of data (e.g., Participant #3-14-113011, a postdoctoral fellow, already had thousands of image files). Quantitative results are stored in Excel and SPSS files, while the audio recordings are in the process of being transcribed. The goal of this project was to preserve and present as much of the material online as possible, and several types of materials presented particular difficulties. Akil, Huda, Maryann E. Martone, and David C. Van Essen. Effective data management is a crucial piece of deploying the IT … The following sections summarize the most salient themes that emerged from the participant interviews. So managing this kind of restricted access is difficult, especially for social scientists when they don’t have multimillion dollar grants. Collecting data digitally eliminates this labor investment and shortens the lag between observation and analysis. With solid data management and data quality technologies, manufacturers can efficiently manage product inventory, and integrate structured and unstructured data from all sources to get an enterprise … Current data management systems must be fundamentally improved so that they can meet the capacity demand for secure storage and transmission of research data. It would be particularly problematical if each collaborator is working under a sponsored project in which their institutions are responsible for data management. While I have some resources as a professor to do some of this on my own, graduate students don’t that are working and in general, how to do this is a problem. 2. Several of the participants in this study were working on collaborative research projects that spanned multiple institutions (e.g., Participants #4-18-121911, #1-03-100511, #3-06-102111, and #1-17-121211), prompting project directors to seek non-university file-sharing options, such as Dropbox or Google Docs. •    If you wanted to go back and work with the data again, what would be the most important information to have? The digital curator said that while the data have been prepared thus far principally for other researchers and therefore require an understanding of geological fieldwork to be meaningful, he envisions an “interactive geologic map” that would be useful to a wide audience. Although she has significant experience working with secondary data sets, she has had no formal training in data curation. After another good introduction to Mobile Computing it In the first part of this three-part blog series, we look at three leading data management challenges: database performance, availability and security. Evans, James A., and Jacob G. Foster. Although digital data collection offers certain efficiencies in moving from the observation to the analysis phase, the associated data management tasks are not easily delegated. The researcher is concerned about her skills in data management. It has been seen that organizations have recognized the importance of big data and are treating data as an asset (probably one of the most valuable of all due to its ability to decide growth trajectory and ability to offer a competitive advantage over competitors), but have failed to draw any fruitful insights from it. Another situation might arise if the principal investigator simply does not dedicate the appropriate time and effort to fulfill responsibilities related to proper data management. •    In the area of privacy and data access control, additional tools should be developed to manage confidential data and provide the necessary security. The best-case scenario encountered during this study was a project at Penn State University that emphasizes ontology development at the beginning of the research process. Although some of these issues stem from a lack of training or knowledge about best practices for data management, the issues cannot be separated from access to adequate infrastructure. Thus, graduate students and junior researchers received some training in data practices specific to that project while working within the lab or project team. •    Documents: MS Word, PDF Additionally, analog data collection requires a significant investment of effort in data entry prior to the analysis phase. Scholars are also grappling with the ethical and philosophical problems of data sharing in a vacuum of coherent policy support for data linking and release. Overpeck, Jonathan T., Gerald A. Meehl, Sandrine Bony, and David R. Easterling. These files not only create storage problems, since up to 100–150 TB are needed for the project, but also require specialized software tools to make the images usable online. •    What are the formats of the data used in this project? •    How large are the files? •    Did you document this system? •    Outsourcing to a data support company, •    Versioning issues This doubt contributes to scholars’ reluctance to allocate time to data preservation and annotation. The thin sections also posed difficulties, because the images needed enough resolution to allow researchers to measure 200–500 grains of the mineral. For example, Participant #2-12-111011, Assistant Professor, Environmental Studies collected data on graffiti during fieldwork and then donated the data to another researcher (see Appendix C, case study #3). Science 221(4611): 609–613. Many a time, organizations ask the IT team to handle and manage the data governance initiatives. b.    Universities should revise their network policies to support multi-institutional research projects. The concern of privacy has been ramped up tremendously over the last 10 or 15 years, and the process of getting permission to analyze data can be difficult, but a trend in social science data is to include more and more information that’s sensitive. Changing the Equation on Scientific Data Visualization. Data Management managers manage these changes, b… Managing large files presents significant challenges for researchers in that university infrastructures typically do not provide adequate storage space or sufficient bandwidth for data access (e.g., Participant #4-25-120511 could not store videos from interviews with study participants on university servers). 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