DataStories · Making Use of Interpretive Judgments of Data Creators
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2019-06-01 → 2021-05-31
- EU contribution
- €212,195
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
DataStories: Making Use of Interpretive Judgments of Data Creators
The DataStories project has asked: What can we learn from understanding the range of interpretive judgments that appear in a dataset? In responding to this question, DataStories demonstrates the potential of structural data analysis to a diverse audience of humanist scholars and students. To inspire and engage the broadest possible audience, Dr. Melanie Feinberg, the DataStories researcher, developed a unique, innovative approach based on narrative accounts of concrete, everyday data situations. These engaging narratives examine and interrogate common experiences such as stepping on a scale, buying groceries in a foreign supermarket, or reacting to a misspelling of one’s name. Through these narratives, the DataStories project provides an account of data as a form of human expression. The DataStories project establishes that, if we hope to act responsibly with data—whether we are collecting it, aggregating it, manipulating it, interpreting it, or making decisions with it—we need to appreciate this human character.
Data: CORDIS, © European Union
Project objective
The creative, situated, and interpretive nature of data collection is well established by scholars across disciplines. Archaeologists recording the color of soil interpret its hue differently. Library catalogers disagree on the title of a book. Collectors of plant specimens provide different kinds of information about the specimen's location: some provide coordinates, others describe landmarks, some record details of the terrain. Interpretive flexibility in data creation occurs despite the use of standardized structures and protocols to enforce consistent data. It even occurs when data is collected by computers. For example, smartphones, fitness trackers, and other devices record the number of steps users take when carrying the device. But although the recording of steps is automatic, people use these devices in flexible, creative ways: they carry them during certain activities but not others, use different devices for different activities, and so on. In the DataStories project, I argue that interpretive judgments of data creators are valuable forms of information, and we should study them and learn from them, not ignore or eliminate them. DataStories seeks to answer the following question: What can we learn from understanding the range of interpretive judgments that appear in a dataset? DataStories has three objectives: 1. To empirically investigate the alternate stories within datasets that arise from data creators’ interpretive judgments. 2. To demonstrate how the variation that arises from data creators’ interpretive judgments is valuable information. 3. To develop a methodological framework for telling these data stories.
Original text from CORDIS.
Participants
- KOBENHAVNS UNIVERSITET · KOBENHAVNCoordinatorDenmark
Links
Data: CORDIS, © European Union