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Elizabeth: University administrator


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The person represented here is not affiliated with DataONE and use of their image does not reflect endorsement of DataONE services.

Name, age, and education: 

Elizabeth is a mid-to-late-career woman who is a professor of biology and the department chair for a regional comprehensive university in Washington State. This university historically focused on teaching. While faculty consider research to be an important part of their work, their research activities are generally restricted to smaller, minimally-funded projects, and they usually have time for research activities only during the summer. Elizabeth was recruited to this university from a research-intensive state university in order to expand the research activities in the biology department, both by changing existing reward structures and by recruiting new, research-focused faculty. However, her efforts are hampered by limited research support and facilities. She is unlikely to be able to recruit faculty who excel according to traditional metrics for the field, such as numbers of publications and impact factors, since those faculty will likely be able to find positions at other institutions which will provide greater support for their research activities. Instead, Elizabeth would like to recruit faculty who excel at new metrics that have demonstrable impact on the field of biology. She consulted with the dean of the college, her current faculty and colleagues elsewhere, and there was broad agreement that the department can and should evaluate the scholarly impact of raw datasets which are contributed to the field. She would also like to perform such evaluations on possible new hires.

Life or career goals, fears, hopes, and attitudes: 

While Elizabeth still maintains a limited research program, she has mostly transitioned to being an administrator. She believes all faculty in higher education should be doing serious, impactful research, which was a primary motivation for accepting the position at this regional comprehensive university. At the same time, she believes that the standards by which we measure “impact” are both flawed and out of date, and they are particularly ill-suited to the circumstances of faculty at a university like hers. She is passionate about finding new ways to support such faculty and to elevate the profile of her new institution, and she believes that the expanding role of cyberinfrastructure in research activities offers some possible solutions, if she can just identify them. She fears that the faculty will resist all of these changes and will resent the push towards greater research productivity, towards new institutional and field-wide metrics, and towards the changing make up of the department. She also fears that the funding and other support for new cyberinfrastructure may not last, especially if few scientists gravitate to these new tools and capacities. These fears mean that she is taking a risk by orienting the department to these new tools and metrics, but she feels strongly that these are risks that must be taken, especially for a department like hers.

A day in the life: 

The biology department has been advertising for new faculty positions, and all job announcements highlight research expectations. The hiring committee narrows the pool of candidates down to a few applicants, but this process is typically based on traditional profiles and metrics. Elizabeth is then tasked with determining the extent to which each of these candidates utilizes key cyberinfrastructure research tools, collaborates with peers located at different institutions, and contributes impactful data to the field. Some candidates have addressed these issues in their applications, so she has some information at hand regarding the locations of their deposited data and any impact metrics they employed, but she has to perform independent analyses as well. She will need to be able to produce some summary statistics of these data-deposition and impact metrics for the hiring committee, so she needs a tool which enables such queries with a minimum of hassle and subjectivity.

Reasons for using DataONE to share and to reuse data
Needs and expectations of DataONE tools: 

In the absence of DataONE, Elizabeth would have to navigate to many different repositories, sign in or create accounts on each one, learn how to query each system appropriately, and then manually compile the disparate data into a coherent document. For Elizabeth, DataONE offers many possible advantages. First, presuming that all of the relevant repositories are member nodes for DataONE, she can access all of them via a single sign-on. She only needs to learn how to work with one set of tools, and she can query multiple repositories and datasets at once. Second, it may be possible to establish some standardized methods of extracting the information she seeks which will expedite such analyses in the future, even as the diversity of member nodes and the data within continues to grow. Third, DataONE may offer the ability to produce summary tables and other outputs based on her analyses, saving considerable time and effort. Fourth, DataONE might make it possible to extract consistent information about data reuse (e.g., citations, derivative datasets, etc) which would otherwise be very difficult to obtain and compare. Elizabeth believes that the DataONE project, and similar ventures, are key to her current needs and to the future of the biological sciences. This also means that Elizabeth has high expectations for DataONE and is likely to react to shortcomings harshly.

Intellectual and physical skills that can be applied: 

Elizabeth is proficient in working with data and databases and has pushed herself to stay on top of new developments in the field. However, she has very limited time and must develop methods to streamline both the analyses and the reports. Because the analytical tasks are not standardized, she feels she must perform the work herself, though she is optimistic that DataONE will make it possible to create rubrics or scripts which will allow her to automate or delegate much of this work in the future. To the extent that Elizabeth is able to extend the functionality of DataONE and create potentially valuable templates for data-related impact metrics, she is interested in sharing those insights and products back to the field.

Technical support available: 

Elizabeth has access to some department-level technical support, as well as support for constructing queries which will extract the information she needs. However, her expectations that she will not really need such support, presuming DataONE has been built to meet the needs of someone like her.

Personal biases about data sharing and reuse (and data management more generally): 

Elizabeth believes that data sharing is crucial, and that researchers should be encouraged to generate and share data and be rewarded accordingly. She also recognizes that such beliefs are not universal, and that changing the culture of science when it comes to tenure and advancement is hard. She is hoping that other peers and institutions will also move in this direction, though she is willing, if not exactly eager, to be a leader in the field. It is her belief that scientists generally lack good data management skills, to the detriment of the profession, and that elevating the professional impact of data sharing, courtesy of projects like DataONE, is the best opportunity to change attitudes and practices.

Comparison of current and DataONE-enabled practices:
Current data discovery: 

Elizabeth needs more efficient and robust data discovery tools, with special emphasis on metadata and paradata about the datasets of interest.

Current data integration: 

Again, Elizabeth's interest is in metrics and metadata about the contributed datasets, not the actual raw data contributed by the applicants, so she doesn't really need to integrate data in the same manner as researchers might want to. However, to the extent that DataONE enables comparisons in these variables for different researchers and their data, this would be enormously helpful.

Current data analyses: 

For Elizabeth, being able to analyze the metadata across datasets and researchers is key. If the metadata can be integrated for ease of analysis, that would be great, but otherwise she is likely to just export the relevant data and analyze them using standard statistical packages.


Written by Ahrash Bissell