EResearch Network

This list of resources is provided by the Digital Library Federation (DLF) for the benefit of past and present DLF E-Research Network [1] cohorts, institutions considering participation in the eRN, and professionals involved in the planning and implementation of research data management services (RDMS).

Welcome

Welcome to the eResearch Guide. The following resources were taken from past cohorts of the DLF eResearch Networks (eRN) and sorted via topics relevant to the eRN community. To add to this guide, please send an email to [info@diglib.org].

If you’re new to the eRN community or just want a refresher - this is the place to start. Below are helpful tips, links and videos to give you a sense of what has been done in past cohorts.

Introduction

Members of each year's eRN cohort participate in webinars (May - October) that discuss topics relevant to research data management. The webinars revolve around a curriculum designed by eRN faculty. Other structural elements of eRN include group activities, consultations, in-person events, evaluation/assessments and digital badges or certificates of completion.

Faculty + CLIR postdoctoral fellows

2016-2018

• Jason Clark (Montana State University)

MLIS, Head of Library Informatics and Computing

• Sara Mannheimer, MSIS (Montana State University)

MSIS, Data Management Librarian

2015

• Jason Clark (Montana State University)

MLIS, Head of Library Informatics and Computing

• Kendall Roark (Purdue University)

Ph.D. in Anthropology, Research Data Specialist

• Margarita Corral (Brandeis University)

Ph.D. in Political Science, Data Analysis Specialist

• Plato Smith (University of New Mexico Albuquerque)

Ph.D. in Library and Information Studies, CLIR Postdoc

2014

• Dr. Chuck Humphrey (University of Alberta Libraries)

Research Data Management Services Coordinator

• Vessela Ensberg (UCLA)

Ph.D. in Cellular and Molecular Biology, Data Curation Analyst

• Inna Kouper (Indiana University)

Ph.D. in Information Science, Assistant Director, Data to Insight Center

• Natsuko Nicholls (Virginia Tech)

Ph.D. in Political Science, Research Data Consultant

• Kendall Roark (University of Alberta)

Ph.D. in Anthropology, Research Data Specialist

curriculum

2016

• Data Advocacy (building advocates, partnerships internal/external, and promoting data literacy)
  • Video: http://clir.adobeconnect.com/p2neutmdcoo/
  • Slides: File:Wright AdvocacyOutreach sm.pptx and File:20160518 Presentation Clark.pptx
• Data Management Planning and Funder Requirements
  • Video: http://clir.adobeconnect.com/p96s4hor04r/
  • Slides: File:ERN-dmp-funder-requirements.pdf
• Digital Scholarship, Digital Humanities, Data Visualization
  • Video: http://clir.adobeconnect.com/p9qmqw2b9w5/
  • Slides: http://literaturegeek.com/2016/07/21/dlf-digital-humanities-what-why-how
• Data Discovery/Metadata and Curation (reusability and preservation)
  • Video: http://clir.adobeconnect.com/p529bfkjbgs/
  • Slides: https://osf.io/zfweh/
• Data Privacy and Data Ethics
  • Video: http://clir.adobeconnect.com/p5zxm5wphjg/
  • Slides: File:ERN-Data-Ethics.pptx
  • Docs: Supporting Ethics in Data Research (workshop materials and video)
• Assessment (Metrics for success with Data Services)
  • Video: https://clir.adobeconnect.com/_a960001693/p2y1qirb8mp/
  • Slides: File:Assessment-Metrics-Research-Data.pptx

2015

• Research Data Management Services at Your Institution
• Data Management Needs
• Library Instruction / Faculty and Student Engagement
• Data Collection, Discovery, and Analysis
• Services Evaluation: Planning and Conducting Evaluations of Your Own

2014

• Development of Research Data Services (RDS) in the Library
• Digital Repositories: Existing Data Curation Options; Institutional and Specialized Data Repositories
• Data Management Needs
• Library Instruction and Faculty and Student Engagement
• Data Collection, Discovery, and Analysis
• Services Evaluation - Planning and Conducting Evaluations of Your Own Efforts

webinars

Webinar recordings are made available to eRN members.

2017 - All webinars will be held from 1:30-3:00 PM ET.

• "Assessment (Metrics for success with Data Services and Digital Scholarship)", with guest Ricky Punzalan of the University of Maryland College of Information Studies. (10/11/2017)
• " Collections as Data: Digital Scholarship, Digital Humanities, Data Visualization", with guest speaker Natasha (Natalya) Noy of Google & Stanford University. (9/13/2017)
• "Data Discovery/Metadata and Curation (reusability and preservation)", with guest speaker Thomas Padilla of UC Santa Barbara. (8/9/2017)
• "Data Management Planning and Funder Requirements", with guest speaker James Neal of the Institute of Museum and Library Services. (7/12/2017)
• "Advocacy and Promotion (building advocates, partnerships internal/external, and promoting data literacy)", with guest speakers Stephanie Wright & Danielle Robinson of Mozilla Science. (7/12/2017)
• "Building an eResearch Services Roadmap (environmental scans and defining RDM, digital scholarship, or other eResearch-related services)", with faculty Jason A. Clark and Sara Mannheimer. (6/10/2017)

2016

• October 19
• September 21
• August 17
• July 20
• June 15
• May 18

2015

• Final 2015 eRN webinar on "Services Evaluation: Planning and Conducting Evaluations of Your Own". Guest speaker was Sara Mannheimer (Montana State). (10/14/2015)
• "Data Curation Repository Models", featuring Nancy McGovern of MIT Libraries as guest speaker. (9/23/2015)
• "Data Collection, Discovery, and Analysis", featuring eRN faculty and postdoctoral fellows Plato Smith, Kendall Roark, and Margarita Corral. (8/12/2015)
• "Library Instruction / Faculty & Student Engagement". Guest speakers, Elaine Martin and Donna Kafel from the UMASS Medical School Library. (7/15/2015)
• "Data Management Needs", with guest speaker Kathleen Fear, Data Librarian, University of Rochester. (6/10/2015)
• Informal debriefing session for those who attended RDAP 2015 (5/17/2015)
• "Research Data Management Services at Your Library" with guest speaker D.Scott Brandt of Purdue. Also includes Network members self-selection on DMS at their institutions. (5/13/2015)
• Informational webinar on the 2015 E-Research Network. 2014 cohort members Kathleen Fear (Univ. of Rochester) and Mayu Ishida (Univ. of Manitoba) discuss their eRN experience. (2/4/2015)

2014

• Final 2014 ERPNMG webinar on services evaluation and the ERPNMG experience. Guest speaker was Micheal Witt (Purdue). He requested that his talk not be recorded nor his slides shared after the presentation. (10/15/2014)
• "Data Collection, Discovery and Analysis." Guest speakers Michele Hayslett (UNC) and John Huck (Univ of Alberta) discuss collection policies for data and collaborating to support access to data through metadata. (9/17/2014)
• "Library instruction / Faculty and student engagement - focus on literacy, embedded librarianship and engagement." Guest speakers Elaine Martin and Donna Kafel (UMass Medical) discuss the NECDMC to teach RDM, and Alisa Surkis (NYU Medical) discusses the development of library data services at an academic medical center. (8/13/2014)
• "Data Management Needs - approaches to assessing the data management needs and developing data management plans." Guest speakers Alicia Hofelich Mohr and Thomas Lindsay from the Univ. of Minnesota discuss "Data Management Needs in the College of Liberal Arts". (7/16/2014)
• Supplemental recording to the June 11, 2014 webinar featuring guest speaker Nancy McGovern, Head of Curation and Preservations Services at MIT Libraries. (7/9/2014)
• "Digital Repositories - Existing data curation options, including institutional and specialized data repositories." Postdoctoral fellows Kendall Roark, Vessela Ensberg, and Inna Kouper and faculty member Nancy McGovern lead and moderate. (6/11/2014)
• "The Development of Research Data Services (RDS) in the Library". Faculty member Chuck Humphrey and postdoctoral fellows Vessela Ensberg and Natsuko Nicholls lead and moderate. (4/23/2014)
• Open House / Interest Webinar (3/12/2014)

About eResearch

eResearch (or e-Research) refers to the use of information technology to support existing and new forms of research. It extends e-Science and cyberinfrastructure to other disciplines like the humanities and social sciences. David Minor of University of California, San Diego Libraries, explains the complexities of cyberinfrastructure [2]. The e-Science Institute (ESI) provides Key Concepts and Terms [3] including definitions for e-Research, cyberinfrastructure, and e-Science (also listed below).

e-Research

The term e-Research here refers to the use of information technology to support existing and new forms of scholarly research in all academic disciplines, including the humanities and social sciences. E-research encompasses computational and e-science, cyberinfrastructure and data curation. E-Research projects often make use of grid computing or other advanced technologies, and are usually data intensive, but the concept also includes research performed digitally at any scale. E-research is useful here as a way to bridge the concept of e-science to other fields such as social science and the humanities. Just as e-science applies large-scale computing to processing vast amounts of scientific research data, e-research could include studies of large linguistic corpuses in the humanities, or integrated social policy analyses in the social sciences.

Cyberinfrastructure

Cyberinfrastructure (or CI) describes research environments that support advanced data acquisition, data storage, data management, data integration,data mining, data visualization and other computing and information processing services distributed over the Internet beyond the scope of a single institution. In scientific usage, cyberinfrastructure is a technological strategy for efficiently connecting laboratories, data, computers, and people with the goal of enabling novel scientific theories and knowledge. The term “cyberinfrastructure” was coined in the U.S. and other countries have different terms for this type of technological infrastructure. Cyberinfrastructure now often includes systems for managing, archiving and preserving data, in addition to data processing, and so can include digital libraries and archives and the software and hardware to support them.

e-Science

E-Science is computationally intensive science carried out in highly distributed network environments, such as science that uses immense data sets requiring grid computing or High Performance Computing to process. The term sometimes includes technologies that enable distributed collaboration, such as the Access Grid, and is sometimes used as an alternative term for Cyberinfrastructure (e.g. e-Science is the preferred term in the UK). Examples of e-Science research include data mining, and statistical exploration of genome and other –omic structures.

A helpful list, 23 Things: Libraries for Research Data [4] put out by the Research Data Alliance (RDA), provides learning resources “to help librarians engage in research data!” A Getting Started in e-Science Guide [5], created by University of Massachusetts Medical School contains comprehensive resources to assist librarians.

Research Data Management

Research Data Management (RDM) refers to the storage, access and preservation of data produced during the entire lifecycle of the data. Eugene Barsky of University of British Columbia gave an overview [6] of the subject in 2015, including providing a glossary of Research Data Canada [7].

Consultation Sessions

Each institutional team will receive a personalized consultation with faculty members and/or CLIR/DLF Data Curation postdoctoral fellows. Consultations are designed to be:

• Individualized (one 90 minute session or two 45 minute sessions) and fully focused on institution and team needs
• Institution-submitted topics and questions
• Confidential

Staying Connected

There are many ways to stay connected with other members of the eRN, for continued networking and information sharing. Beyond connecting with others via the CLIR Connect portal [8], Google group [9], or LinkedIn group [10], some may be interested in a new CLIR MemberCentric Phone Application [11]. This app helps eRN members to connect and collaborate, participate in communities of interest, and access up-to-date content and news from DLF and CLIR.

Another way to stay connected: follow DLF on Twitter @CLIRDLF [12] and tweet about your eRN experiences, webinars, etc. using #eResearchNetwork [13].

Outreach/Inreach

advocacy

Research data consultation and collaboration was the topic of the first eRN webinar in 2015. D. Scott Brandt of Purdue University states that multiple approaches are needed to advocate for data curation, including accommodating to a wide range of sub-disciplinary data characteristics and sharing practices. Webinar 1 Group Activity [14] gave institutional participants prompts about different elements to consider when developing research data services. It used the framework taken from Digital Curation Centre (DCC) Guide, How to Discover Requirements for Research Data Management Services [15].

partnerships

As part of a 2012 eRN capstone project, a collaboration matrix [16] was created. The matrix addresses institutional needs and how internal and external partnerships can assist in community building.

Part of building partnerships is identifying liaison and subject librarians that can provide services related to existing data and information in specific areas. For an eRN group activity in 2014 [17], institutions were suggested to identify liaison librarians along with sharing a summary of the existing or emerging responsibilities of those librarians and their contributions to data services.

internal skills

A SWOT analysis may be first step in identifying strengths, weaknesses, opportunities and threats for your data research. Oregon State University provides an example of their SWOT Analysis [18]. Adapted from Anne R. Kenneya and Nancy Y. McGovern, Five Organizational Stages Applied to eResearch [19] is a way of thinking about where an organization is in terms of its involvement with eResearch. Stages include:

• Acknowledge - determining that eResearch is of interest locally
• Act - initiating relevant projects
• Consolidate - shifting from projects to programs
• Institutionalize - incorporating the broader environment and rationalizing programs
• Externalize - embracing inter-institutional collaboration and dependencies

Data Literacy

teaching

Data Information Literacy (DIL) Project Website [20]. This project helps raise awareness of research data management and curation issues among researchers, through developing and implementing data information literacy (DIL) instruction programs for graduate students. The website of the project has gathered updated information and development of the researcher interview instruments, the data management training curriculum, and research publications.

Data Management Planning

Data Management Planning or DMP outlines how data is handled before, during and after a research project. There are a wide-range of institutional resources out there to assist researchers in managing their data. A guide was created by eRN in 2014 for institutions to share material [21]. Other examples include University of Maryland’s Quick Start Guide to DMPs [22] and Stanford University's DMP Guide [23]. Another helpful resources is the Data Management Practices in Social Sciences report [24] and Managing and Sharing Data Guide [25] written by Veerle Van den Eynden, Libby Bishop, Laurence Horton and Louise Corti of University of Essex in 2010.

If creating a research data services website, Colgate University provides a sitemap layout [26]

strategic agenda

Strategic agendas for research data services guide the development of data services at libraries. A strategic guide is informed by best practices in data management at peer universities. For an example, check out Oregon State University Libraries and Press Strategic Agenda for Research Data Services [27]. Other examples are agendas put out by University of Arizona [28], University of Manitoba [29], and Northwestern University [30].

data management curriculum

University of Michigan Medical School Lamar Soutter Library provides an instructional tool for teaching data management best practices to undergraduates, graduates, and researchers in health sciences, sciences, and engineering disciplines. New England Collaborative Data Management Curriculum [31] website provides seven online instructional modules that align with the National Science Foundation’s data management plan recommendations and addresses universal data management challenges.

funding requirements

As stated in the Stanford DMP website [32]: Many funding agencies require a DMP with every funding request. Each agency or directorate creates its own set of policies for data management. Consult the documents below to find out what you will need to include in the DMP for your research proposal.

• NIH: NIH Data Sharing Policy and Implementation Guidance [33]
• NSF: Data Management Policies [34] and FAQ on Data Management and Sharing [35]
• NOAA: Data Sharing for NOAA Grants Procedural Directives [36]
• Institute of Education Sciences: Data Sharing Implementation Guide [37]
• Gordon and Betty Moore Foundation: Data Sharing Philosophy and Data Sharing and Management Plans [38]

University of California, San Diego, provides Sample NSF Data Management Plans [39]

Data Discovery

metadata

Metadata helps provide context to research projects. Open Data Support provides a slideshare [40] that explains metadata, outlines the metadata lifecycle, discusses metadata quality and management.

curation

The Interuniversity Consortium for Political and Social Research (ICPSR) provides a Data Management and Curation Guide [41] on research data quality, preservation, access, confidentiality, and citation.

governance of research data

MacKenzie Smith, MIT Research Director, shares experience with data governance, data archiving, copyright, licenses, contracts, metadata and other topics relevant to eRN in this presentation [42].

data discovery

Ontologies for eScience and why we need metadata explained in presentation [43] by Melissa Haendel of Oregon Health & Science University Library in 2011.

preservation

ICSPR’s Guide to Social Science Data Preparation and Archiving Guide [44] provides a comprehensive planning schedule for archiving and preserving data. ICSPR also has a Digital Preservation section [45] that has more information on digital preservation standards and a glossary of terms.

Johns Hopkins University provides a presentation about their data management services [46], including data conservancy objectives and data preservation.

Digital Scholarship

data visualization

According to ESI, Data visualizations are visual representations of data, or abstract information. In the context of e-Science data visualization is closely related to scientific visualization, an interdisciplinary branch of science primarily concerned with the visualization of three dimensional phenomena (architectural, meteorological, medical, biological, etc.), where the emphasis is on realistic renderings of volumes, surfaces, illumination sources, and so forth, perhaps with a dynamic (time) component. Visualizations and simulations are a key part of scientific communication in the digital era, and require sophisticated software to execute (i.e. the visualizations are often not static files that can be captured and preserved like a digital image).

Data Privacy and Ethics

sharing research data

The Conundrum of Sharing Research Data [47] by Christine Bordgman of University of California, Los Angeles, addresses the complexities of sharing data - “an intricate and difficult problem”.

environmental justice

Peter Rogers of Colgate University Libraries uses maps and space as a case study for using data to make changes [48].

data horror stories

Mismanaged data can cause big problems. Kristin Briney, data services librarian of University of Wisconsin-Milwukee, compiles a list of data horror stories [49] as does faculty associate of University of Wisconsin, Dorothea Salo [50].

Assessment

The DLF Assessment interest group [51] seeks to engage the community in developing best practices and guidelines for various kinds of digital library assessment.

The Data Census: Assessing Data Services at MSU [52], presented by Sara Mannheimer of Montana State University in 2015, provides information on survey development, assumptions and results. Lessons learned included researching other institutional data surveys and assessment frameworks, balance length of survey with depth of information gathered, advocacy for information libraries want to collect and seeking IRB approval to enable data sharing.

Some institutions provided a self-assessment of their data services. University of Manitoba released an 18-page self-assessment report in 2012 [53] with intentions to continue assessing through contextual interviews and surveys. University of Rochester contributed a report on their services in 2013 [54]. Context Interviews can be tracked using an Interview Tracking Sheet Template [55]

Perform your own self-assessment by completing Self-Assessment Questionnaire [56]

About this wiki

If interested in adding to this wiki, please contact [info@diglib.org]. Formatting for this wiki should be consistent with other resources on the page. Each resource added should include a short 1-2 sentence description. eRN faculty should include updated curricula and webinar information under the matching headings. Four user personas were created when making this document, to ensure that the wiki is designed to be useful for the variety of eResearch Network users. They should be considered when posting new content: Library Administrator, Data Librarian, Non-Librarian Person and New to Field.

This guide was compiled on March 4, 2016 by Brandon Patterson, with help from Oliver Bendorf, Bethany Nowviskie, Jason Clark and Sara Mannheimer. The guide consists of files collected from the 2011 - 2016 eRN cohorts housed at DLF and from the CLIR Connect community libraries: eResearch Network and DLF eResearch Network 2015.

Suggestions for improvement:

• Include resources to fill gaps in data literacy, metadata and digital scholarship/humanities
• Visual elements (icons) added to topics using Font Awesome Plus Extension [57]
• Collect content from CLIR Connect discussions, seek permissions for past cohort webinars to share
• Collect and post curricula prior to 2014
• The addition of tags labeled “beginner”, “intermediate” and “advanced” would differentiate which resources are best for which level of learner