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<h3>Course outline: Scientific data management for ecology and evolution</h3> <h4>Instructors: Sally Taylor, Raymond Ng, Diane Srivastava, and postdocs from the Living Data Project</h4> <p><strong>Schedule</strong> Tuesday, Thursday Nov2-Dec4 excluding the week of Nov 9</p> <ul> <li>10:30-12:00h British Columbia </li> <li>12:30 -14:00h Saskatchewan, Manitoba </li> <li>13:30-15:00h Ontario, Quebec.</li> </ul> <p><strong>Course description</strong> </p> <p>This course will develop best practices in data management in ecology and evolution research. We will use a combination of instruction, in-class activities and projects to guide students through all parts of the research data lifecycle, starting with the collection and storage of data, progressing through the organizing of data (database design, “tidy” data principles, data versioning), the cleaning of data (quality assessment, geospatial and taxonomic data standards), and ending with the sharing of data (metadata and documentation, and archiving and accessing data in digital repositories following the new FAIR principles). Each student will work progressively through the course on an individual data management plan for the data they will collect - or have already collected - for one of their research projects. As well, students will work in small groups on preparing an existing biological dataset for archiving using R scripts. This course, one of the first such courses in Canada specifically geared to ecology and evolution, will give students the tools for managing their own research data as well as rescuing previously collected data.</p> <p><strong>Pre-requisites</strong><br> - Graduate student conducting thesis research specifically in Ecology or Evolutionary Biology - In order to create a Data Management Plan (major part of course grade), you are at a stage where you have an idea of what data you are <em>likely</em> to collect for part of your thesis, or you have already collected data for your thesis or a previous research project. - Introductory R programming experience (i.e. base R) </p> <p><strong>Delivery format</strong><br> - 8 sessions, 1.5 hours per session - each session generally includes a lecture and hands-on component, each varying in length among sessions</p> <p><strong>Assessment:</strong></p> <p>60% Data management plan for your research project, completed individually </p> <p>40% Group project on preparing a dataset for archiving </p> <p><strong>Required materials</strong><br> - Personal computer with videoconferencing ability - Internet access </p> <p><strong>Workload</strong></p> <p>In general, students in each Living Data Project module should anticipate approximately 30-45 work hours, in line with the normal expectations for one credit of coursework. This will comprise the following activities: 12 hours formal instruction in a class setting, 4 hours of individual/small group mentoring, 5 hours of preparatory reading, up to 15 hours on required assignments. Assignments are completed progressively as the course proceeds to allow instructor feedback on drafts, with a final version of major projects due within a week of course completion</p> <p><strong>Logistics</strong></p> <p>For help on course material, students are encouraged to contact their primary mentor as follows (but if you cannot reach then, feel free to contact another instructor): </p> <ul> <li>UBC-V and Regina* - Ellen Bledsoe (ellen [dot] bledsoe [at] weecology [dot] org)</li> <li>UdeM, McGill*, SFU, Toronto - Joey Burant (jburant[at] uoguelph [dot] ca) </li> <li>UBC-O - Mauro Sugawara (sugawara [at] zoology [dot] ubc [dot] ca) </li> <li>Carleton, Manitoba, UQAM - Bruno Carturan (bruno [dot] carturan [at] ubc [dot] ca)</li> </ul> <p>*<em>including students doing transfer credits via these institutions</em></p> <p>Participation in this course requires adherence to the Living Data Project <a href="https://osf.io/u2abx/wiki/home/" rel="nofollow">Code of Conduct</a></p> <p>We have set-up a <strong>course chat forum</strong> and an <strong>assignment submission portal</strong> on OSF. This is the preferred platform, but the instructional team can also accommodate students who prefer not to use OSF. Instructions on how to set-up and use OSF for these purposes is found <a href="https://osf.io/ghwua/wiki/Instructions%20for%20Students/" rel="nofollow">here</a> and we are happy to help you with any questions. </p> <p><strong>International students at UBC</strong> are directed to read <a href="https://osf.io/k4x2f/wiki/home/" rel="nofollow">this</a> statement</p> <p><strong>Schedule</strong></p> <p>Workshop 1 (Sally Taylor and other instructors): Introduction of instructors and course outline. Why manage research data? Overview of the data life cycle and data management planning, storage and backup of active datasets, non-sensitive vs. sensitive data, file naming and version control. In-class activities: creating <a href="https://assistant.portagenetwork.ca/" rel="nofollow">DMP Assistant</a> account and file naming/versioning</p> <p>Workshop 2 (Raymond Ng): Designing databases: entity-relationship model. In-class activity: develop entity-relationship models for example datasets </p> <p>Workshop 3 (Raymond Ng): Designing databases: choosing relations to create and constraints. In-class activity: relations and constraints for example datasets</p> <p>• <em>Before Workshop 4, students should have completed the “Storage and Backup” and “Data Collection” sections of their Data Management Plans (DMPs), including designing a database for their data.</em></p> <p>Workshop 4 (Diane Srivastava): Tidy data principles, functions for joining, subsetting, reshaping and summarizing data in R, working with strings, the trouble with time. In-class activity: formation of groups and introduction to datasets, use R functions on these datasets for joining, subsetting, reshaping and summarizing datasets and manipulating strings and time data. </p> <p>Workshop 5 (Diane Srivastava): Quality control after the data is collected, geospatial and taxonomic data standards. In-class activity: group project on cleaning datasets</p> <p>Workshop 6 (Sally Taylor): Describing datasets (documentation, metadata and coding), metadata standards used in ecology and evolution (e.g. EML, Dublin core, json). In-class activity: describing dataset using a specific standard (e.g. Darwin Core, EML)</p> <p>• <em>Before Workshop 7, students should submit their group project on data organizing and cleaning</em></p> <p>Workshop 7 (Sally Taylor): FAIR principles for scientific data management, data sharing practices in ecology and evolution, archiving data in digital repositories, licensing datasets including types of creative common licenses, linking new data to previously collected data, accessing and citing data from published data repositories. In-class activity: identifying suitable repositories for depositing your datasets or for finding datasets</p> <p>• <em>Before Workshop 8, students should submit their Documentation and Metadata, Data Preservation, Data Sharing and Reuse sections of their DMP.</em></p> <p>Workshop 8 (Diane Srivastava and other instructors): Loss of data in ecology and evolution; Responsibilities and Resources, and Ethics and Legal Compliance for data management; Data rescue examples and tools. In-class activity: assistance with individual DMPs</p> <p><em>• Within one week of the course ending, students should complete their final Data Management Plans, including Responsibilities and Resources, and Ethics and Legal Compliance sections</em></p>
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