(B-5) Determine Data Sources/Discuss Data Transfer Procedures

Your institution will need to discuss and formulate the initial data transfer plan and procedures for populating the system and the timing of on-going data transfers. When importing data, administrators can allow certain activities in locked sections to be edited. Please contact Interfolio for more information.

Following are the key areas to be considered:

Base Data

Uploading the base data will create user accounts and populate most biographical/demographic data in the Personal Information and Contact Information input sections, and will also populate the Current Position section of the Profile Form. The course catalog and Courses Taught data for each term will also be added.

The base datasets are typically exports from institutional sources. Consider the following when planning for exports:

  • The timing of ongoing data transfers
  • Options for exporting data from HR systems (e.g. Banner, Datatel, PeopleSoft, etc.)
    • Direct: automated data transfer to the FACULTY180 server
    • Indirect: manual file upload via SFTP
    • Manual file upload: managed by the Institutional Software Administrators
  • Options for importing scholarly citations (e.g. Academic Analytics, Web of Science, Google Scholar)
  • The importance of this data being in sync with similar data, as held and reported from other university systems

The following is a list of base data in the recommended load order for implementation purposes. This order may be different for FACULTY180 maintenance than during the implementation process.

Data File Name
 Description
 Result
(1) Units
 Hierarchical structure and names/abbreviations of units (e.g. Colleges, Departments, etc.)
 Hierarchical organizational structure in FACULTY180
(2) Faculty
 Biographical and demographic information, contact information, current position, etc.
 Faculty accounts in FACULTY180
(3) Current position Populates the current position section.  Supports multiple position titles per faculty member.
 Relevant current position information shows up in the faculty's profile form, within Faculty180.
(4) Secondary unit assignment If a faculty member has more than one appointment, this file can be used to bulk set for all faculty.
 Users who have more than one role, or administrative level, at an institution, will be able to access either through one set of login credentials through Interfolio.
(5) Faculty classification Categories for faculty—There are three standard faculty classifications: (1) employment status, (2) tenure status, (3) faculty rank. Additional classifications can be added for specific purposes, such as accreditation reporting. As faculty classifications are added to the database, the fields are added to the field list options.

 Faculty classification to tag faculty (This is optional during the implementation process, but is a critical database maintenance upload.)
(6) Course prefixes
 Prefix, prefix name, primary unit
 First of three datasets used to populate Teaching section
(7) Courses
 List of courses that have or might be taught — typically pulled from course catalog
 Second of three datasets used to populate the Teaching section
(8) Courses Taught
 Term and year
 Third of three datasets used to populate the Teaching section.
(9) Committees
 Standing and ad hoc committees (Note: not required with uncontrolled committee structure) Option for a ‘controlled’ list is selected by clicking Admin > Set Up > School > Details > Miscellaneous
 A controlled list that populates Institutional Committee section. (From a controlled list, faculty will select committee name from a dropdown list or select other and input a committee name.)
(10) Support accounts Accounts can be created in bulk for users in support roles (ex. Administrative assistant for a Chair / Dean)
 Support accounts, administrative only, accounts, are available to those who are serving Faculty180 users through a support based role. 
(11) Scholarly Outlets
 Scholarly outlet list used in the title field in the Scholarly Outlet input section
 Autocomplete listing for the Scholarly Outlet title field. (Optional part of implementation process)
(12) Scholarly Outlet Metrics
 Metrics assigned to scholarly outlets
 Scholarly outlet metrics, like impact factor

Legacy Data (Parsed Data)

Legacy data is faculty activity data that is stored in an institutional repository or a competitors faculty activity reporting system. Examples of legacy data include teaching, research, service, and professional development activities stored in an electronic format. Because FACULTY180 collects data in parsed data fields, the data fields can be mapped and exported in .csv or tab delimited formats with fields in FACULTY180.

Legacy data conversion should be discussed during the contract phase to determine if additional setup fees might apply. Clients who are aware of the need for legacy data conversion should consult withtheir Interfolio Account Representative regarding the potential cost of this work.

There are several variables related to data migration (e.g., communication turnaround, turnaround schedules, data file size, data formatting, etc.) that can make it challenging to estimate the timeframe for completion. However, with proper communication, full disclosure about the scope of transfers, and participation on the part of the client, the successful completion of data migration can be managed on schedule and in a timely manner.

In order to facilitate this process, it is important that both the institution and Interfolio understand the workflow and expectations for this data conversion.

Vita Data (Non-Parsed Data)

Faculty members on your campus have significant amounts of activity data contained in non-parsed formats, such as Microsoft Word and PDF; therefore, the data fields cannot be mapped to FACULTY180.As a result, different methods must be utilized to migrate this data to FACULTY180. Your institution needs to consider which data will be migrated.

Options include:

  • Collect no prior vita data.
  • Collect some prior vita data - limited by time (only collect for the last five years) and area (only collectgrants, journal publications and creative productions).
  • Collect all prior vita data.

If some or all prior vita data will be migrated, determine which methods will be used.

Options include:

  • Faculty members input data starting with the current term and for each term moving forward
  • Faculty members input a portion of their data from prior terms (typically three to seven prior years) and for all future terms
  • Other methods for data input for prior periods:
    • In the first year, faculty members should only input data for the current term into FACULTY180. Each year thereafter, faculty members should input data for the current year plus two to five previous years.
    • Use data entry assistants (student employees or administrative staff members) to input data.
    • Input only some data for faculty members to review and complete. For example, have data entry employees enter only the titles, locations, and dates of journal publications, and have faculty members complete the remaining fields, such as the method of review.
    • Input scholarly contributions and creative productions in bulk from reference management software (e.g. Zotero and Endnote) or online citation databases (e.g. Academic Analytics and Web of Science) using FACULTY180's bibliographic importing capabilities.
    • Outsource the process of data migration. (Interfolio has a recommended vendor. Contact your Interfolio Project Manager or Account Representative for more information.)

Vendor data

Faculty data are available in a variety of databases provided from third-party databases and applications. Most of these have APIs that allow their data to be integrated into FACULTY180. Examplesinclude:

  • Publication databases, such as Academic Analytics, EBSCO, Google Scholar, MEDLINE/PubMed, SciVal, Web of Science
  • Reference management software, such as Endnote, RefWorks, Zotero
  • Grant databases, such as eTrac and Kuali
  • Course evaluation solutions, such as CourseEval, EvaluationKIT, Scantron, SmarterServices
  • Research information systems, such as Converis, Pure, Symplectic