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- Creator:
- York, Jeremy, Gutmann, Myron, and Berman, Francine
- Description:
- The data were collected as part of the Stewardship Gap project, an 18-month study to investigate how research data and creative outputs supported by public or non-profit funding in the United States are being stewarded. These data were collected as part of a literature search of sources about research data stewardship and relate most directly to work describing “What We Know About the Stewardship Gap.” In this work, we categorized “gaps” in stewardship identified in the literature, how the gaps were related to one another, and efforts to measure and develop metrics for the gaps.
- Keyword:
- digital curation, digital preservation, research data, data stewardship, and data sustainability
- Citation to related publication:
- York, J. et al. 2016. What Do We Know About The Stewardship Gap? https://deepblue.lib.umich.edu/handle/2027.42/122726
- Discipline:
- Other
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- Creator:
- Mirshams Shahshahani, Payam
- Description:
- Investigating minimum human reaction times is often confounded by the motivation, training, and state of arousal of the subjects. We used the reaction times of athletes competing in the shorter sprint events in the Athletics competitions in recent Olympics (2004-2016) to determine minimum human reaction times because there's little question as to their motivation, training, or state of arousal. The reaction times of sprinters however are only available on the IAAF web page for each individual heat, in each event, at each Olympic. Therefore we compiled all these data into two separate excel sheets which can be used for further analyses.
- Keyword:
- minimum reaction time, sprinter, Olympics, Athletics, sex difference, starting block, and false start
- Citation to related publication:
- Mirshams Shahshahani P, Lipps DB, Galecki AT, Ashton-Miller JA (2018) On the apparent decrease in Olympic sprinter reaction times. PLoS ONE 13(6): e0198633. https://doi.org/10.1371/journal.pone.0198633
- Discipline:
- Engineering, Health Sciences, Science, Other, and General Information Sources
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- Creator:
- Yakel, Elizabeth, Suzuka, Kara, and Frank, Rebecca
- Description:
- These data collection and analysis protocols and the attribute list are part of a larger research project, the Institute of Museum and Library Services # LG-06-14-0122-14. funded "Qualitative Data Reuse: Records of Practice in Educational Research and Teacher Development." As such, our research questions concern data reuse and data curation: 1. Data Reuse: What are the dynamics of the data reuse lifecycle (from selection of data through the reuse of data) in a qualitative digital educational archive? 2. Data curation: What special issues are involved in curating digital qualitative data for reuse? • How can qualitative data archives best support data reusers throughout the data reuse lifecycle? • What aspects of this experience are informative for other types of qualitative data archives? The overall project employed mixed methods and collected interview, observational, and trace data from data reusers of video records of practice in education and repositories holding video records of practice. The interview protocol and interview codeset relate to the 44 interviews conducted with researchers and teacher-educators who have reused digital video records of practice as qualitative data for research and/or teaching.
- Keyword:
- Data curation, Data reuse, and Digital records of practice in education
- Citation to related publication:
- Frank, Rebecca D, Tyler, Allison R. B., Gault, Anna, Suzuka, Kara, and Yakel, Elizabeth, (forthcoming), "Issues of Privacy in Qualitative Video Data Reuse," International Journal of Digital Curation , Suzuka, K., Frank, R., Crawford, E. & Yakel, E. (2018). Video re-use in mathematics teacher education. In E. Langran & J. Borup (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 329-336). Washington, D.C., United States: Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/182544/., Frank, R. D., Chen, Z., Crawford, E., Suzuka, K. and Yakel, E. (2017), Trust in qualitative data repositories. Proceedings of the Association for Information Science and Technology, 54: 102–111. http://dx.doi.org/10.1002/pra2.2017.14505401012, and Frank, R. D., Suzuka, K., & Yakel, E. (2016). Examining the Reuse of Qualitative Research Data: Digital Video in Education. In Archiving Conference (Vol. 2016, pp. 146–151). Washington, DC: Society for Imaging Science and Technology. https://doi.org/10.2352/issn.2168-3204.2016.1.0.146
- Discipline:
- Other
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- Creator:
- Dillahunt, Tawanna R., Lam, Jason, Lu, Alex, and Wheeler, Earnest
- Description:
- Today’s Information and Communication Technologies (ICTs) support job searches, resume creation and the ability to highlight employment skills on social media. However, these technological tools are often tailored to high-income, highly educated users, and white-collar professionals. It is unclear what interventions address the needs of job seekers who have limited resources, education, or who may be underserved in other ways. We gathered insights from past literature and generated ten tangible design concepts to address the needs of underserved job seekers. We then conducted a needs validation and speed dating study to understand which concepts were most viable among our population. We found that the three most preferred concepts immediately addressed job seekers’ most practical needs. and Per reviewer feedback, we aim to improve the utility of this publication to other scholars by including our research materials here. This dataset includes the interview script, storyboards that were used in the needs validation study, the demographics survey/questionnaire, and the consent form.
- Keyword:
- Design, Underserved job seekers, Storyboards, Speed dating, Employment, and Needs Validation
- Citation to related publication:
- Tawanna R. Dillahunt, Jason Lam, Alex Lu, and Earnest Wheeler. 2018. Designing Future Employment Applications for Underserved Job Seekers: A Speed Dating Study. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS '18). ACM, New York, NY, USA, 33-44. DOI: https://doi.org/10.1145/3196709.3196770 and http://www.tawannadillahunt.com/wp-content/uploads/2018/05/disfp453-dillahuntA.pdf
- Discipline:
- Other
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- Creator:
- Cheng, Hao Fei, Hecht, Brent , Wheeler, Earnest, Wang, Xinyi, Zhu, Haiyi, and Dillahunt, Tawanna R
- Description:
- The sharing economy has quickly become a very prominent subject of research in the broader computing literature and the in human–computer interaction (HCI) literature more specifically. When other computing research areas have experienced similarly rapid growth (e.g. human computation, eco-feedback technology), early stage literature reviews have proved useful and influential by identifying trends and gaps in the literature of interest and by providing key directions for short- and long-term future work. In this paper, we seek to provide the same benefits with respect to computing research on the sharing economy. Specifically, following the suggested approach of prior computing literature reviews, we conducted a systematic review of sharing economy articles published in the Association for Computing Machinery Digital Library to investigate the state of sharing economy research in computing. We performed this review with two simultaneous foci: a broad focus toward the computing literature more generally and a narrow focus specifically on HCI literature. We collected a total of 112 sharing economy articles published between 2008 and 2017 and through our analysis of these papers, we make two core contributions: (1) an understanding of the computing community’s contributions to our knowledge about the sharing economy, and specifically the role of the HCI community in these contributions (i.e. what has been done) and (2) a discussion of under-explored and unexplored aspects of the sharing economy that can serve as a partial research agenda moving forward (i.e. what is next to do).
- Keyword:
- Collaborative and social computing, Human-computer interaction interaction, and Human-centered computing
- Citation to related publication:
- Dillahunt, T. R., Wang, X., Wheeler, E., Cheng, H. F., Hecht, B., & Zhu, H. (2017). The Sharing Economy in Computing: A Systematic Literature Review. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW), 38:1-38:26. https://doi.org/10.1145/3134673
- Discipline:
- Other
-
- Creator:
- James, David A.
- Description:
- An Excel spreadsheet listing the information recorded on each of 18,686 costume designs can be viewed, downloaded, and explored. All the usual Excel sorting possibilities are available, and in addition a useful filter has been installed. For example, to find the number of designs that are Frieze Type #1, go to the top of the frieze type 2 column (column AS), click on the drop-down arrow and unselect every option box except True (i.e. True should be turned on, all other choices turned off). Then in the lower left corner, one reads “1111 of 18686 records found”. Much more sophisticated exploration can be carried out by downloading the rich and flexible Access Database. The terms used for this database were described in detail in three sections of Deep Blue paper associated with this project. The database can be downloaded and explored. HOW TO USE THE ACCESS DATABASE 1. Click on the Create Cohort and View Math Trait Data button, and select your cohort by clicking on the features of interest (for example: Apron and Blouse). Note: Depending on how you exited on your previous visit to the database, there may be items to clear up before creating the cohorts. a) (Usually unnecessary) Click on the small box near the top left corner to allow connection to Access. b) (Usually unnecessary) If an undesired window blocks part of the screen, click near the top of this window to minimize it. c) Make certain under Further Filtering that all four Exclude boxes are checked to get rid of stripes and circles, and circular buttons, and the D1 that is trivially associated with shoes. 2. Click on Filter Records to Form the Cohort button. Note the # of designs, # of pieces, and # of costumes beside Recalculate. 3. Click on Calculate Average Math Trait Frequency of Cohort button, and select the symmetry types of interest (for example: D1 and D2) . 4. To view the Stage 1 table, click on Create Stage 1 table. To edit and print this table, click on Create Excel (after table has been created). The same process works for Stages 2, 3.and 4 tables. 5. To view the matrix listing the math category impact numbers, move over to a button on the right side and click on View Matrix of Math Category Impact Numbers. To edit and print this matrix, click on Create Excel, use the Excel table as usual.
- Keyword:
- Group Theory, European regional costume, Symmetry, Ethnomathematics, European folk costume, and Classification of designs
- Citation to related publication:
- James, D. A., James, A. V., & Root, M. J. (2017). Symmetry in European folk costumes. Ann Arbor: University of Michigan. Retrieved from the Deep Blue institutional repository website: http://hdl.handle.net/2027.42/136161
- Discipline:
- Other
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- Creator:
- Okullo, Dolorence, Yan, Xiang (Jacob), Data Driven Detroit, Veinot, Tiffany C., Gomez-Lopez, Iris N., and Goodspeed, Robert
- Description:
- The food environment is: 1) The physical presence of food that affects a person’s diet; 2) A person’s proximity to food store locations; 3) The distribution of food stores, food service, and any physical entity by which food may be obtained; or 4) A connected system that allows access to food. (Source: https://www.cdc.gov/healthyplaces/healthtopics/healthyfood/general.htm) Data included here concern: 1) Food access; and 2) Liquor access. Spatial Coverage for most data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area, Michigan, USA. See exception for grocery store data below.
- Keyword:
- Food Deserts, Census tract level, Full-Line Grocery Stores, Modified Retail Food Environment Index (MRFEI), Farmer’s Markets, Spatial Measures, and Fast Food Establishments
- Discipline:
- Social Sciences, Health Sciences, and Other
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- Creator:
- Reference USA, City of Detroit, ESRI, Data Driven Detroit, and Veinot, Tiffany C.
- Description:
- Active living resources include spaces and organizations that facilitate physical activity, including 1) park land, 2) recreation areas (including parks, golf courses, amusement parks, beaches and other recreational landmarks); and 3) recreation centers (including gyms, dancing instruction, martial arts instruction, bowling centers, yoga instruction, sports clubs, fitness programs, golf course, pilates instruction, personal trainers, swimming pools, skating rinks, etc.) Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
- Keyword:
- Recreation Areas, Park Land, Census tract level, Metropolitan Detroit, Spatial Measures, Recreation Centers, and Michigan
- Discipline:
- Health Sciences, Social Sciences, and Other