├── .gitignore ├── .here ├── README.md ├── data-science-tenure.Rproj ├── mizzou ├── 2015-11-13-P&T-TT-bylaws.docx └── 2019-04-19-P&T-TT-revised.docx └── resources ├── peerj-preprints-3204.bib └── peerj-preprints-3204.pdf /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .DS_Store 3 | .Rhistory 4 | .RData 5 | -------------------------------------------------------------------------------- /.here: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkearney/data-science-tenure/5f261548e6f463352cf8b35c2443ff4d0119ad37/.here -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # data-science-tenure 2 | 3 | 4 | ## [Resources](resources) 5 | 6 | + Waller LA. 2017. [Documenting and evaluating Data Science contributions in academic promotion in Departments of Statistics and Biostatistics](resources/peerj-preprints-3204.pdf). PeerJ Preprints 5:e3204v1 https://doi.org/10.7287/peerj.preprints.3204v1 7 | 8 | *In the 1990s, Ernest Boyer of the Carnegie Foundation for the Advancement of 9 | Teaching articulated the value of different types of interdisciplinary 10 | scholarship in his highly cited Scholarship Reconsidered: Priorities for the 11 | Professoriate (latest, expanded edition: Boyer et al. 2016). This report, very 12 | familiar to university administrators such as deans, provosts, and presidents, 13 | but often less well known by junior faculty, highlights the value of multiple 14 | types of faculty contributions within academia, specifically noting four types 15 | of scholarship. The first, scholarship of discovery, mirrors the standard 16 | disciplinary model of original research advancing knowledge within a field, 17 | often evidenced by peer reviewed publications in established disciplinary 18 | journals and success in obtaining competitive research funding. A second type, 19 | scholarship of integration, recognizes innovative synthesis of information 20 | across traditional disciplines, across subdivisions within a discipline, or 21 | across time. Such scholarship creates new knowledge through novel links between 22 | specific concepts, tools, and studies from disparate fields of inquiry. Boyer's 23 | third type, scholarship of application (sometimes called scholarship of 24 | engagement), goes beyond simply applying existing tools (as would a technician) 25 | to value the deep collaborative contributions in creating advances in 26 | interdisciplinary studies, particularly within a team science framework. The 27 | fourth type, scholarship of teaching and learning, values the systematic study 28 | of pedagogical methods for the transfer and creation of new knowledge between 29 | faculty members, colleagues, and the next generation of scholars.* 30 | 31 | *These four categories provide rich support for many current efforts within the 32 | field of Data Science and its link to academic departments of Statistics and 33 | Biostatistics and topics for the continuing critical conversations between the 34 | chair, the candidate, and senior faculty. The four types of scholarship also 35 | provide a context for collecting, presenting, and reviewing scholarly 36 | contributions of junior faculty. The concept of the scholarship of integration 37 | is immediately extensible to Data Science, particularly with respect to linking 38 | heterogeneous data components and developing new analytic tools, hence enabling 39 | new lines of inquiry. Documentation of such contributions within a promotion 40 | dossier is somewhat non-traditional and may include citable data within 41 | repositories and software packages/toolboxes in addition to peer-reviewed 42 | publications and funded grants. The scholarship of application is evidenced by 43 | interdisciplinary publishing, the creation of data repositories and complex data 44 | sets, and clear contributions unique to the candidate within an 45 | interdisciplinary team. Data Science research contributions often are linked 46 | deeply to the intersection of Boyer's scholarships of integration and 47 | application, and it will be advantageous to highlight the impact of these 48 | contributions within this context.* 49 | 50 | *The rapid development of training programs, concentrations, and degree programs 51 | within the area of Data Science offers multiple opportunities for the 52 | scholarship of teaching and learning. Success in this area extends well beyond 53 | simply teaching new courses and advising students, it involves research and 54 | discovery on the modes and methods of instruction and learning, an area of clear 55 | interest in the statistical education research community, but only just 56 | developing in the broader area of Data Science (National Academies 2014).* 57 | 58 | *Boyer's categories provide a valuable framework for organizing and presenting a 59 | candidate's scholarly contributions for review. The candidate can organize 60 | materials under Boyer's categories in the CV, and mention them in their Personal 61 | Statement. A department chair can frame contributions in light of discovery, 62 | integration, application, and teaching to external reviewers and when presenting 63 | a candidate's promotion for consideration by senior faculty and promotion 64 | committees. Senior faculty can use Boyer's categories as a lens through which to 65 | view accomplishments and assess impact of a candidate's scholarly work.* 66 | -------------------------------------------------------------------------------- /data-science-tenure.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: No 4 | SaveWorkspace: No 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: knitr 13 | LaTeX: XeLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | 18 | BuildType: Package 19 | PackageUseDevtools: Yes 20 | PackageInstallArgs: --no-multiarch --with-keep.source 21 | PackageRoxygenize: rd,collate,namespace 22 | -------------------------------------------------------------------------------- /mizzou/2015-11-13-P&T-TT-bylaws.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkearney/data-science-tenure/5f261548e6f463352cf8b35c2443ff4d0119ad37/mizzou/2015-11-13-P&T-TT-bylaws.docx -------------------------------------------------------------------------------- /mizzou/2019-04-19-P&T-TT-revised.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkearney/data-science-tenure/5f261548e6f463352cf8b35c2443ff4d0119ad37/mizzou/2019-04-19-P&T-TT-revised.docx -------------------------------------------------------------------------------- /resources/peerj-preprints-3204.bib: -------------------------------------------------------------------------------- 1 | @article{10.7287/peerj.preprints.3204v1, 2 | title = {Documenting and evaluating Data Science contributions in academic promotion in Departments of Statistics and Biostatistics}, 3 | author = {Waller, Lance A}, 4 | year = 2017, 5 | month = aug, 6 | keywords = {Team Science, Academic promotion, Tenure, Professional developent}, 7 | abstract = { 8 | The dynamic intersection of the field of Data Science with the established academic communities of Statistics and Biostatistics continues to generate lively debate, often with the two fields playing the role of an upstart (but brilliant), tech-savvy prodigy and an established (but brilliant), curmudgeonly expert, respectively. Like any emerging discipline, Data Science brings new perspectives and new tools to address new questions requiring new perspectives on traditionally established concepts. We explore a specific component of this discussion, namely the documentation and evaluation of Data Science-related research, teaching, and service contributions for faculty members seeking promotion and tenure within traditional departments of Statistics and Biostatistics. We focus on three perspectives: the department chair nominating a candidate for promotion, the junior faculty member going up for promotion, and the senior faculty members evaluating the promotion package. We contrast conservative, strategic, and iconoclastic approaches to promotion based on accomplishments in Data Science. 9 | }, 10 | volume = 5, 11 | pages = {e3204v1}, 12 | journal = {PeerJ Preprints}, 13 | issn = {2167-9843}, 14 | url = {https://doi.org/10.7287/peerj.preprints.3204v1}, 15 | doi = {10.7287/peerj.preprints.3204v1} 16 | } -------------------------------------------------------------------------------- /resources/peerj-preprints-3204.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkearney/data-science-tenure/5f261548e6f463352cf8b35c2443ff4d0119ad37/resources/peerj-preprints-3204.pdf --------------------------------------------------------------------------------