Project Plan

Proposal: PDF on eScholarship

Timeline: 1 Oct 2014 - 1 Oct 2015

Project Communication

Personnel

  • CDL
    • Patricia Cruse (5%), Project oversight
    • John Kratz (in-kind), Field research
    • Carly Strasser (25%), PM for field research
  • NCEAS / DataONE
    • Amber Budden (in-kind), DataONE Community Engagement
    • Matt Jones (in-kind), DataONE oversight, Manage development
    • Dave Vieglais (in-kind), DataONE oversight
    • Developer (NCEAS, TBD) (17%), DataONE integration
  • PLOS
    • John Chodacki (in-kind), Product Director, PLOS oversight
    • Martin Fenner (100%), Technical Lead, DLM developer
    • Jennifer Lin (50%), PM
    • Kristen Ratan (in-kind), Publisher, PLOS oversight

High-level Project Summary

  1. design, develop, and prototype a reference model for data metrics
  2. test mechanisms of automatic tracking
  3. explore ways in which the raw DLM data be delivered to drive data discovery across data types and research questions
  4. prototype a flexible report for funders, institutions, and researchers to share DLM results.

Work Components

1. DLM Field Research (Oct-Jan)

  • What: Surveys, interviews, focus groups to determine requirements for DLM
  • Who: CDL with PLOS input
  • Input: list of questions/things we want to know from the community
  • Output: metrics design & requirements

2. Data Usage Tracking (Nov-Feb)

  • What: Extend DataONE usage tracking capacity
  • Who: DataONE with PLOS input
  • Input: existing usage API
  • Output: extended usage API (COUNTER)

3. Data Activity Aggregation (Dec-Apr)

  • What: Formulate a set of metrics to text; extend technology
  • Who: PLOS
  • Input: design & requirements
  • Output: DLM application

4. DLM Integration & Presentation (May-Jun)

  • What: Develop tools for the community to use metrics
  • Who: PLOS
  • Input: ALM Reports source code, DLM data
  • Output: DLM Reports application & widgets

Bibliometric Analysis (July-Sep)

  • What: Analyze, write up results from project
  • Who: PLOS, CDL, DataONE
  • Input: data design and collection results
  • Output: final report and recommendations