This readme.txt file was generated on <20201109> by Cody Hennesy. ------------------- GENERAL INFORMATION ------------------- 1. R1 Digital Scholarship Program Survey Dataset, 2020 2. Author Information Principal Investigator Contact Information Name: Benjamin Wiggins Institution: University of Minnesota, Twin Cities Email: benwig@umn.edu Associate or Co-investigator Contact Information Name: Cody Hennesy Institution: University of Minnesota, Twin Cities Email: chennesy@umn.edu ORCID: 0000-0002-9410-9810 Associate or Co-investigator Contact Information Name: Brian Vetruba Institution: University of Minnesota, Twin Cities Email: bvetruba@umn.edu ORCID: 0000-0001-7998-9330 Associate or Co-investigator Contact Information Name: Alexis Logsdon Institution: University of Minnesota, Twin Cities Email: logs0002@umn.edu ORCID: 0000-0003-3406-5577 Associate or Co-investigator Contact Information Name: Emily Janisch Institution: University of Minnesota, Twin Cities Email: janis036@umn.edu 3. Date of data collection 20200507 - 20200811 4. Geographic location of data collection (where was data collected?): Online, Qualtrics Survey distributed via email; Respondents are from Digital Scholarship programs at Carnegie Classification R1: Doctoral Universities. -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CCO (Public Domain) 2. Links to publications that cite or use the data: Forthcoming 3. Links to other publicly accessible locations of the data: none 4. Links/relationships to ancillary data sets: none 5. Was data derived from another source? No If yes, list source(s): 6. Recommended citation for the data: see DRUM record --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: dss_data_q4_12.csv Short description: Respondent answers to Questions 4 to 12 from the survey. Questions 6, 9, and 11 include inductive codes applied and used for analysis. Survey respondents full-text answers to qualitative questions are removed to preserve anonymity. B. Filename: dss_data_q13.csv Short description: List of codes applied to answers for Question 13 (in alpha order by original answer). C. Filename: dss_data_q14_17.csv Short description: Inductive codes applied to respondent's free text answers from Questions 14 to 17 from the survey. Respondent's answers are not included in the data to protect participants' anonymity. 2. Relationship between files: See survey_questions.txt for the questions from the survey. 3. Additional related data collected that was not included in the current data package: Answers for Questions 1 to 3 from the survey are not included to protect respondent privacy and anonymity. 4. Are there multiple versions of the dataset? No -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: A Qualtrics survey was distributed on May 4, 2020 via email from author one’s email address to potential respondents. Data was collected primarily for the 6 weeks following. One new respondent was identified and filled out the survey on August 11, 2020, after which the survey was closed. Authors one and two performed inductive coding for Questions 6, 9, 11, 13, 14, 15, 16, and 17; those codes are included in the dataset. 2. Methods for processing the data: * Data for Questions 4, 5, 7, 8, 10, and 12 are the raw data as exported to CSV from the Qualtrics survey. * Data for Questions 6, 9, and 11 represent the codes applied by authors one and two for each data point. Coding was performed in shared spreadsheets. * Data for Question 13 was compiled into a single list and re-ordered using Python/Pandas. Those answers were exported to a CSV which author one used to apply inductive codes for each answer. Only the codes are included here. * Data for Questions 14 to 17 consist only of the codes applied by authors one and two for each respondent answer. Coding was performed in shared spreadsheets and then downloaded to CSV. 3. Instrument- or software-specific information needed to interpret the data: any tool that can read tabular data via CSV (e.g., Excel, Python Pandas) 4. Standards and calibration information, if appropriate: none 5. Environmental/experimental conditions: none 6. Describe any quality-assurance procedures performed on the data: none 7. People involved with sample collection, processing, analysis and/or submission: See "Author information," above. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: dss_data_q4_12.csv ----------------------------------------- 1. Number of variables: 9 2. Number of cases/rows: 71 3. Missing data codes: Empty cells represent unanswered survey questions and/or a lack of codes applied in inductive coding process. 4. Variable List A. Name: Q4. Employee numbers (columns Q4_1_1 to Q4_6_1) - Description: Number of various types of employees (see row 2 for types) - Values are numeric. Whole numbers representing the number of employees. B. Name: Q5. Job class contributions (columns Q5_1_1 to Q5_8_1) - Description: Overall percentage contribution for each employee type (see row 2 for types) - Values are numeric. Numbers represent the percentage of contribution. Each row for Q5 columns should add up to 100 percent. C. Name: Q6. Funding units (columns Q6_1 to Q6_8) - Description: Codes applied to represent respondent's free-text answers to units funding DS programs. - Values are strings. Each values represents the codes applied by study authors to each survey response. D. Name: Q7. Funding percentage (columns Q7_1_1 to Q7_8_1) - Description: Funding percentage submitted by respondents for each unit identified in Q6. - Values are numeric. Each value represents the percentage of funding provided by each unit in Q6. E.g., The unit listed in Q6_3 corresponds to the funding percentage listed in Q7_3_1. Each row for Q7 columns should add up to 100 percent. E. Q8. Categories of support (Q8_1_1 to Q8_4_1) - Description: The respondents' estimated percentage of support contribution for each of four broad areas: Research, Teaching, Outreach & Engagement, and Other (see row 2 for categories) - Values are numeric. Each value represents the percentage of support for each category. Each row for Q8 columns should add up to 100 percent. F. Name: Q9. Support topics (columns Q9_1_code to Q9_5_code_2) - Description: Codes applied to represent respondent's free-text answers regarding topics most commonly supported. Respondents could list up to five topics, and select responses received up to two codes for a single response. Q9_1_code and Q9_1_code_2, for example, list codes applied for the first of five topics respondents could contribute. - Values are strings. Each value represents a code applied by study authors to each survey answer. G. Q10. Technology supported (columns Q10_1 to Q10_5) - Description: Respondents free-text responses listing up to five technologies they most commonly support. Responses were normalized for analysis. - Values are strings. Each value is a free-text response from those surveyed. H. Q11. Outreach types (columns Q11_1_code to Q11_5_code) - Description: Codes applied to represent respondent's free-text answers regarding most common modes of outreach. Respondents could list up to five kinds of outreach, and some responses received up to two codes for a single response. - Values are strings. Each value represents a code applied by study authors to each survey answer. I. Q12. Audiences (columns Q12_1_1 to Q12_9_1) - Description: Percentages of use by various audience categories. See row 2 for specific audience category. - Values are numeric. Each number represents a percentage listed for each audience type. Percentages for each row under Q12 columns should add up to 100 percent. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: dss_data_q13.csv ----------------------------------------- 1. Number of variables: 1 2. Number of cases/rows: 193 3. Missing data codes: none 4. Variable List A. Name: Q13. Duplicative services (column q13_code) - Description: Free-text responses regarding duplicative/similar programs were compiled into a single list and then sorted alphabetically. That list was then coded by author one. - Values are strings. Each value provided here is a code applied by author one for each response. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: dss_data_q14_17.csv ----------------------------------------- 1. Number of variables: 4 2. Number of cases/rows: 61 (respondent/rows are not listed here when individual respondents did not answer Q14-Q17) 3. Missing data codes: Empty cells represent lack of code applied for a given answer. 4. Variable List A. Name: Q14. Successes (columns q14_1_code to q14_8_code) - Description: Codes applied by authors one and two to free text answers to Q14. Each response received up to eight separate codes (thus 8 columns for Q14). - Values are strings. Values provided here are the codes applied by author one for each response. B. Name: Q15. Challenges (columns q15_1_code to q15_6_code) - Description: Codes applied by authors one and two to free text answers to Q15. Each response creceived up to six separate codes (thus 6 columns for Q15). - Values are strings. Values provided here are the codes applied by author one for each response. C. Name: Q16. Assessment (columns q16_1_code to q16_8_code) - Description: Codes applied by authors one and two to free text answers to Q16. Each response received up to eight separate codes (thus 8 columns for Q16). - Values are strings. Values provided here are the codes applied by author one for each response. D. Name: Q17. Priorities (columns q17_1_code to q17_10_code) - Description: Codes applied by authors one and two to free text answers to Q17. Each response received up to ten separate codes (thus 10 columns for Q17). - Values are strings. Values provided here are the codes applied by author one for each response. ----------------------------------------- DOCUMENTATION FILE: survey_questions.txt ----------------------------------------- Lists the survey questions as exported from Qualtrics.