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Data Services : Service Guidelines

NYU Libraries and IT support for quantitative, qualitative, GIS, and research data management

General Information

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Data Services home page

Data Services continues virtual services in Fall 2020. During our working hours, we will respond to requests via e-mail and hold consultations via Zoom. Chat for immediate assistance during our staffed hours.

Staffed Hours: Fall 2020
   Mondays:       12pm - 6pm
   Tuesdays:       12pm - 6pm
   Wednesdays: 12pm - 6pm
   Thursdays:     12pm - 6pm
   Fridays:          12pm - 4pm

To contact us, submit a request or email data.services@nyu.edu.

If you've met with us before, tell us how we're doing.

Virtual Help

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Overview

Data Services offers consultations, teaching, and support for data. learning, analysis, and research. Our staff strives to provide quality advice, information, and support for those working with our supported software platforms, finding and interpreting data, and implement practices for data management and reproducibility. The following expectations are intended to prepare patrons and students to interact with our services productively.

General Expectations and Guidelines

  • Patrons of all Libraries services must abide by the Libraries Code of Conduct. We reserve the right to deny service at our discretion for violations of the Code in consultations or workshops.

  • As a general rule, Data Services provides consultation specifically for software and coding platforms we support. In most cases, we do not offer deep methodological support, though we do offer support in best practices for data management, and strategies for data finding. We also routinely suggest resources to support methodological choices, assist learners as they understand the implications of those choices, and help learners execute these choices within a supported software package.

  • Data Services supports requests directly connected to the research and educational goals of the university. Examples include scholarly research, course assignments and projects, dissertation/thesis work, grant proposals, and other related forms of intellectual inquiry. We are not able to assist with projects involving individual entrepreneurship, outside employment, externships, or paid consultancies.

  • Data Services provides consultations and research data management advice for data projects involving classified or sensitive data, but we ask that patrons do not provide us with access to that data or introduce such data into the computing environment of the consultation.

  • Though Data Services consults on content, principles, and literacies present in student assignments, the service is not intended to be a homework help or tutoring support service. We support learners in applying concepts and techniques to their own work independently. Staff may suggest that users take our introductory tutorials, use sample datasets as guidelines, reference learning resources and documentation, or recommend that learners confer with advisors (i.e. teaching assistants, professors, department advisors) to receive help with an assignment.

  • Because we want to maximize our availability to all members of the NYU community, we do not provide protracted, for-hire, or contract-based consultation services.

  • Data Services staff typically does not provide detailed, step-by-step assistance or troubleshooting with software installation. We do our best to couple software access and licenses with helpful documentation so that users can install programs effectively, and when possible, we will point to recommended resources for users who are installing software, particularly in cases where software needs are occasioned by remote learning.

Expectations for Scheduled Consultations

  • We do not provide individualized, out-of-the-box consultations or introductions to software platforms. In such cases, users will be asked to register for upcoming tutorials and introductory classes. We do our best to distribute our classes across a range of days and times.

  • Scheduled consultations are limited to one hour. We expect that complicated or time-intensive requests be broken into multiple consultations if necessary.

  • Provide at least 24 business hours notice when scheduling an appointment. This allows us to match requests with our staff availability and find the person best prepared to help.

  • Confirm your appointment and provide project files, sample data, and questions in advance. Having access to files, working code, or sample data enables our consultants to prepare in advance and make the time more effective.

Expectations for Receiving In-Person or Virtual Help

The Data Services lab on the 5th floor of Bobst is staffed with consultants who are able to provide assistance with data-related questions or challenges. Generally, these consultants specialize in a particular software or service area, and we post their schedules publicly to communicate availability. Virtual desk assistance is most appropriate for questions that are relatively bounded in nature (e.g., opening or converting a file, exporting a project, understanding the format of your output, etc.). If a question becomes more complicated or involved, our consultants will instruct you to fill out a consultation request and reserve a dedicated time to work in greater depth.

Statement on Repeat Consultations

Current members of the NYU community, which includes students, faculty, and staff, are not limited in the amount of times they can schedule consultations or receive virtual or on-the-spot help. However, we expect that our consultations drive toward the goals and outcomes of the metaliterate learner. That is, we hope that those who attend many consultations at Data Services recognize that learning is a process and that reflecting on errors or mistakes leads to new insights and discoveries. We expect that our consultations will cultivate the ability to determine both the knowledge gained and the gaps in understanding.

Other Expectations

Many of the specific teams and service areas within Data Services have expectations that are more specific to the guidelines addressed here. See: