Undergraduate Student Assistant Intro to Data Science Course Materials
The D-Lab is hiring an Undergraduate Student Assistant for the NSF-funded project, Undergraduate Data Science at Scale. This part-time position for work-study eligible students starts at $18/hr for approximately 5hrs per week, with the possibility of continuing for multiple semesters. The undergraduate student will serve on the project’s Introduction to Data Science Course Materials Team to assist in developing a submission portal and collecting and organizing Introduction to Data Science course materials from our network of colleges and universities. As part of these responsibilities, the student will meet regularly with a graduate student partner to ensure the quality and organizational structure of this growing repository.
To apply, interested Berkeley undergraduate students should
complete this survey by Friday, October 7, 2022
Data Science students and candidates in their sophomore and junior years from diverse or underrepresented backgrounds are encouraged to apply.
Qualtrics is a powerful online tool available to Berkeley community members that can be used for a range of data collection activities. It is designed to make web surveys easy to write, test, and implement, but the software can be used for data entry, training, quality control, evaluation, market research, pre/post-event feedback, and other uses with some creativity.
This overview will introduce a simple workflow using the system with an orientation to the main interfaces for web survey design, sample management, corresponding with sample members, and exporting data at the end of the field period. We will cover the basics of designing a survey instrument using the Qualtrics platform, such as randomization, survey flow, and how to maximize the quality of responses.
The Berkeley Initiative for Transparency in the Social Sciences (BITSS) and the UC Berkeley Library is excited to announce the Open Research Seminar (ORS), a webinar series to promote and share knowledge about the use of tools and practices for transparency and reproducibility in social science research. The ORS will be an opportunity for researchers to showcase applications of specific tools and practices (e.g., developing a lab protocol for reproducibility, building a website to catalog open datasets, etc.) or evaluations of the effectiveness of existing tools and practices (e.g., results-blind peer review and publication bias). All webinars will be free and open to register to the general public.
Legal Literacies for Text Data Mining, Cross-Border (LLTDM-X)
Call for Roundtable Participants
Join us for a project on cross-border TDM research (NEH funded)
Stipend Available | Deadline Nov. 4 at 5pm
Are you a U.S.-based researcher who has done or wanted to do a computational text analysis (or “text data mining” / TDM) project on materials held in countries outside the U.S.? Have you ever collaborated with a colleague outside of the U.S. on a TDM project? Or conducted TDM on content created by people living outside the U.S.?
You can help us help you, by sharing your experiences in a virtual roundtable discussion if you’ve ever done, or tried to do, any of the above. What law, policy, privacy, or ethics problems popped up, and what questions did you face or do you anticipate facing?
The LLTDM-X team seeks to compensate 10 additional U.S.-based (living or working in U.S.) humanities and social sciences researchers with up to $800 stipends for discussing the legal and ethical issues they face or will face when conducting their cross-border TDM research.
Not sure if your TDM research counts as “cross-border”?
We created this brief explanatory video to help you. https://youtu.be/Y-dF9x-y8ow
If after watching the video, you think we’re describing your research and you want to participate in the LLTDM-X roundtable, please submit an application no later than 5 p.m. PST November 4, 2022.
- TextXD Conference: Call for Presentations -
TextXD is an interdisciplinary conference on computational text analysis, spanning theory, applications, tools, and ethics. It strives to bring together researchers from different fields who normally wouldn’t cross paths to share their expertise and perspectives with each other across domains — spanning human languages, literature, social sciences, law, health and medicine, computer science, and beyond.
The conference is hosted by the Berkeley Institute for Data Science and
partnering with D-Lab to provide training on foundational computational text analysis tools.
The event will be held in-person from December 5 to 7.
Executive Director of Educational Initiatives (6083U) #42697
The Division of Computing, Data Science, and Society (CDSS)
The executive director for educational initiatives will manage the new Master of Machine Learning (MML) program management office, a developing unit within the Division of Computing, Data Science, and Society. The Master of Machine Learning will be an online self-supporting graduate professional degree program designed to train current software engineers and others with undergraduate degrees in computer science, data science, statistics, and related fields in the specialized techniques and knowledge required to become machine learning engineers. The MML program management office will oversee admissions, student affairs, career services, academic planning, curriculum development, marketing, and communications related to this new program.
First Review Date: September 29, 2022 - Open Until Filled Apply Here
- XLab for Social Science Research -
The eXperimental social
science Laboratory (XLab)
Xlab supports UC Berkeley’s world class research by providing resources such as access to participant pools, experiment coordination, payment support, access to softwares, grants, and more. It also provides technical and administrative support. For more information about Xlab, click here!
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