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.