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  • Iterations of the RDS Unit_2012-2022

    Document thumbnail: Iterations of the RDS Unit_2012-2022
    Type:
    Document
    Description/Abstract:
    This document is supplemental materials for the book chapter "Playing in the Same Sandbox: Collaborations on Data Management, Research Technologies, and Research Computing" in the book Cases on Establishing Effective Collaborations in Academic Libraries authored by Amy Koshoffer and Amy Latessa
    Creator/Author:
    Koshoffer, Amy
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    04/18/2022
    Date Modified:
    04/18/2022
    Date Created:
    2022-04-18
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • UC Data Day 2020 Presentations

    Generic Work thumbnail: UC Data Day 2020 Presentations
    Type:
    Generic Work
    Description/Abstract:
    The files in this work represent the presentations and workshop content from the 5th UC Data Day held 2020-10-23. The theme was “World Changing Data: How Digital Data Will Change Our Future”. The Keynote speaker was Glenn Ricart, of US Ignite - "Smart Runs on Data" Interactive Panel featuring: Michael Dunaway (moderator) - Whitney Gaskins (Asst Dean, CEAS - Incl Excellence & Comm Engagmnt) - Zvi Biener (Assoc Professor, A&S Philosophy) - Prashant Khare (Asst Professor, CEAS - Aerospace Eng & Eng Mechanics)- Sam Anand (Professor, CEAS - Mechanical Eng) - Achala Vagal (Professor Clinical - GEO, COM Radiology Neuroradiology) Power Sessions: George Turner - Indiana University - High-Performance Computing at UC Erin McCabe - University of Cincinnati - Text Mining, Natural Language Processing & AI link to slides -  https://bit.ly/dataday_slides link to code -  https://bit.ly/dataday_code Videos of the day can be found on the UC Libraries STRC1 youtube channel -  https://www.youtube.com/c/STRC1/videos
    Creator/Author:
    Koshoffer, Amy
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    11/03/2020
    Date Modified:
    12/04/2020
    Date Created:
    2020-10-23
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • COVID 19 literature NLP models – Viral outbreak models formatted

    Dataset thumbnail: COVID 19 literature NLP models – Viral outbreak models formatted
    Type:
    Dataset
    Description/Abstract:
    The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.
    Creator/Author:
    Koshoffer, Amy; Hagedorn, Philip ; Latessa, Jenny; Lee, James; Power, Margaret; Luken, Sally; McCabe, Erin; Wu. Danny; Washington, Dorcas; Kannayyagar, Suraj, and Edgerton, Ezra
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    10/29/2020
    Date Modified:
    11/05/2020
    Date Created:
    2020-07
    License:
    CC0 1.0 Universal

  • COVID 19 literature NLP models - Viral outbreak model centrality files

    Dataset thumbnail: COVID 19 literature NLP models - Viral outbreak model centrality files
    Type:
    Dataset
    Description/Abstract:
    The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.
    Creator/Author:
    Koshoffer, Amy; Wu, Danny; Latessa, Jenny; Lee, James; Luken, Sally; McCabe, Erin; Edgerton, Ezra; Washington, Dorcas; Kannayyagar, Suraj, and Powers, Margaret
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    10/29/2020
    Date Modified:
    11/05/2020
    Date Created:
    2020-07
    License:
    CC0 1.0 Universal

  • COVID 19 literature NLP models – Viral outbreak topic tuning

    Dataset thumbnail: COVID 19 literature NLP models – Viral outbreak topic tuning
    Type:
    Dataset
    Description/Abstract:
    The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.
    Creator/Author:
    Koshoffer, Amy; Wu, Danny; Latessa, Jenny; Kannayyagar, Suraj; Luken, Sally; McCabe, Erin; Edgerton, Ezra; Washington, Dorcas; Lee, James; Powers, Margaret, and Hagedorn, Philip
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    10/30/2020
    Date Modified:
    11/05/2020
    Date Created:
    2020-07
    License:
    CC0 1.0 Universal

  • RDAP "Ask Me Anything" Townhall webinar “Thriving as a Data Information Professional”

    Document thumbnail: RDAP "Ask Me Anything" Townhall webinar “Thriving as a Data Information Professional”
    Type:
    Document
    Description/Abstract:
    The first webinar for the 2019-2020 RDAP year occurred on Oct 30th from 3:30 to 4:30 EST was “Ask me Anything” town hall meeting “Thriving as a Data Information Professional”. A panel of experienced data informational professional shared experiences and expertise answered questions about daily work as a data information professional. Panelists were: Wendy Mann - Geroge Mason (director of digital scholarship center) Lynda Kellam - UNC Greensbro, now at Cornell Megan Sapp Nelson - Purdue (data information literacy) Jon Wheeler - New Mexico (data curation librarian) Christie Ann Wiley - U of IL (research data services librarian) Notes were taken by Wanda Marsolek No recording is available
    Creator/Author:
    Koshoffer, Amy; Sapp Nelson, Megan ; Mann, Wendy ; Marsolek, Wanda ; Wiley, Christie Ann; Kellam, Lynda , and Wheeler, Jon
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    05/07/2020
    Date Modified:
    05/07/2020
    Date Created:
    2019-10-03
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • RDAP "Ask Me Anything" Townhall webinar “Using APIs for Non-Programmers”

    Media thumbnail: RDAP "Ask Me Anything" Townhall webinar “Using APIs for Non-Programmers”
    Type:
    Media
    Description/Abstract:
    This is a video recording of the RDAP webinar “Using APIs for Non-Programmers” held on April 22, 2020 @ 1:00 pm EST as part of the RDAP Town Hall “Ask Me Anything” series webinar. Webinar Description: Application programming interfaces -- "APIs" -- are a key way that systems make functionality and detailed information available. Long available only to software developers, modern APIs are quite accessible to the human user, even those with no programming skills! This webinar will cover: - why you might want to access APIs - how to formulate API requests using a web browser - how to do the same using other freely available tools - how to interpret and work with responses - how to translate API documentation to API requests - examples of APIs drawn from repository, preprint, and identifier systems - next steps to crawling information from APIs The webinar is specifically targeted at those working in libraries and library-like settings, who may want or even need to access APIs, but who have no programming or command line experience. Greg Janée is director of the Data Curation Program at the University of California, Santa Barbara, and a certified Carpentry instructor. He's been a researcher and developer in the areas of digital libraries and digital preservation for over twenty years. As a software developer, most recently he was the principal developer of the California Digital Library's EZID persistent identifier service; earlier, he was the principal developer of UCSB's Alexandria Digital Library, Gazetteer Protocol, and related technologies
    Creator/Author:
    Koshoffer, Amy and Janée, Greg
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    04/23/2020
    Date Modified:
    04/23/2020
    Date Created:
    2020-04-22
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • Data Management for GIS

    Media thumbnail: Data Management for GIS
    Type:
    Media
    Description/Abstract:
    This video is the recording of the fourth webinar RDAP “Ask me Anything” town hall series entitled “Data Management for GIS projects”, held on February 26, 2020. It was jointly sponsored by RDAP and the University Consortium for Geographic Information Science (UCGIS). UCGIS was host this webinar. Webinar Description: With federal and other funding agencies wanting to ensure long term access to research output, data management planning for GIS projects has great importance. This webinar shares insight on lessons learned through GIS projects and highlights best practices for different steps in project exploration and creation. Also in this webinar, a geology researcher using GIS techniques and collecting geospatial data will explain how she communicated her use of best practices through a data management plan for a successfully funded NSF grant. Attendees will understand the importance of well-written data management plans, how to put those plans into action through implementing data management planning and reproducible research best practices, and how library engagement can help with data management planning. Presenters: Amy Koshoffer, University of Cincinnati; Jennifer Latessa, University of Cincinnati; and Paula Figueiredo, North Carolina State University Slide deck is also included. About UC–GIS: The University Consortium for Geographic Information Science (UCGIS) is a non-profit organization that creates and supports communities of practice for GIScience research, education, and policy endeavors in higher education and with allied institutions. We are the professional hub for the academic GIS community in the United States, with partnerships extending this capacity abroad.
    Creator/Author:
    Koshoffer, Amy; Paula Marques Figueiredo, and Jennifer Latessa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    02/28/2020
    Date Modified:
    02/28/2020
    Date Created:
    2020-02-27
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • RDAP "Ask Me Anything" Townhall webinar "What is different about Clinical Data?"

    Media thumbnail: RDAP "Ask Me Anything" Townhall webinar "What is different about Clinical Data?"
    Type:
    Media
    Description/Abstract:
    This webinar was presented to the RDAP community on January 29, 2020 @ 1 pm EST. The goal of the webinar was to raise awareness of the special requirements of clinical data for data management. Managing data has a number of common principles that get applied to each subject domain. As a result, it’s easy to start consideration of data with a disciplinary subject and data structure. But in the health sciences and medicine there is a key issue that has to be asked first: Is it clinical research data? Before delving into metadata standards, FAIR principles, or sharing, a health sciences data librarian often starts with the clinical/nonclinical distinction. But what’s so special about clinical data? What do we even mean when we talk about clinical data? This session will introduce basic concepts in clinical data management, processes that are commonly used by researchers looking to do clinical data research. We will explore how a health sciences library can provide patron support on local infrastructure for accessing and using clinical data for research. We will also provide resources for further exploration by librarians and patrons. Attendees will come away with: (1) a better understanding of basic terminology and data workflows in clinical research data management; (2) a sense of typical workflows that happen with the clinical research data lifecycle; (3) an introduction to systems in clinical data collection and analysis, particularly REDCAP; and, (4) at least one answer to the question of “What’s different about clinical?” When it comes to data. Date: January 29, 2020 @ 1 pm EST Presenters: Nina Exner is the research data librarian at Virginia Commonwealth University, serving both the medical and core campuses. Before joining VCU in 2017, she was a Researcher and Grant Support Services librarian, supporting all disciplines. She received her Master’s in Library Science in 1997 from North Carolina Central University and her Ph.D. in information science from UNC-Chapel Hill in 2019. ORCID 0000-0002-8746-8364 Christy E Navarro joined UC Davis in August of 2019 as Health Library Informaticist at Blaisdell Medical Library. Prior to that she designed privacy programs for large health systems in California, helped implement ecosystems of research data sharing, developed best practice guidelines in data management, and served as the resource of choice for privacy, data de-identification, and research data questions. She brings 20 years of expertise in healthcare informatics, health information exchange, process improvement, program design, population health improvement, human subject research and patient & consumer privacy. Christy has a Master of Science in Health Informatics from UC Davis Health and a Bachelor of Science in Business with an emphasis in Management Information Systems from CSU Sacramento. Lori D Sloane has been working at the University of New Mexico Health Sciences Center since 1990. For the first 23 years, she has been the IT support for researchers on campus. Lori brought REDCap to the University of New Mexico as one of the first 13 sites to pilot this software. She transitioned into a Data Manager position for the Health Sciences Library and Informatics Center seven years ago.
    Creator/Author:
    Koshoffer, Amy; Sloane, Lori D ; Navarro, Christy E , and Exner, Nina
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    01/31/2020
    Date Modified:
    02/21/2020
    Date Created:
    2020-01-29
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • RDAP "Ask Me Anything" Townhall webinar on Institutional Data Policies

    Media thumbnail: RDAP "Ask Me Anything" Townhall webinar on Institutional Data Policies
    Type:
    Media
    Description/Abstract:
    This webinar was presented to the RDAP community on December 2, 2019 at 12 pm EST. The goal of the webinar was to hear from the RDAP community about their experiences with institutional research data policies that regulate the ownership, management, and transfer of research data in an institution. The webinar organizing committee was Sophie Hou, Amy Schuler, and Clara Liebot invited panelists were: Kristin Briney, Biology & Biochemistry Librarian, Caltech University, Heather Coates, Digital Scholarship & Data Management Librarian / Co-Director, Center for Digital Scholarship, Indiana University Purdue University Indianapolis, Abigail Goben, Information Services and Data Management Librarian Associate Professor, University of Illinois-Chicago, Jonathan Petters, University Libraries Data Management Consultant and Curation Services Coordinator, Virginia Polytechnic Institute and State University. Background/Use Case (provided by Clara Llebot of Oregon State University): I work in a research intensive university as the library data management specialist. I have worked occasionally on data policies during my time here, like when we wrote the policy that regulates dataset reviews in our institutional repository. These policies were usually flexible, informative, and a helpful tool for me. Earlier this year I was asked to be part of a committee that would create an institutional research data management policy in our institution. I was thrilled that the library was being asked to participate, and at the same time terrified that I had no idea what I was getting into. I have been generally interested in concepts around data ownership, the interactions between copyright and data, decision making regarding research data, etc., but I felt unprepared.An institutional research data policy is, from my perspective, a policy that affects a lot of people, and that has the potential of changing behaviors and research practices in a way that I am definitely not used to. We are still beginning the process of creating the policy, so right now what I have is mostly questions, not answers, about what an institutional research data policy should say. Main Discussion Questions: 1. Motivations for the policy Is an institutional research data policy necessary in any institution? What are the issues/gaps that we are trying to address through this policy? What should be the goal of an institutional research data policy? 2. Roles and responsibilities Who should be involved in creating this kind of policy? How should the faculty be involved in the creation of this policy? How should a research data policy be enforced? How should students be affected by this policy? 3. Outcomes of existing data policies What is the type of content addressed in an institutional research data policy? Should ownership be a part of it? Are research data policies encouraging or deterring open data? What can we do, when writing this type of policy, to make clear that the university supports open data? Or should this be in different policies? What are some examples of situations that are easier/better because there is a research data policy at an institution?
    Creator/Author:
    Koshoffer, Amy
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    12/03/2019
    Date Modified:
    01/31/2020
    Date Created:
    2019-12-02
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • De-identifying Data

    Media thumbnail: De-identifying Data
    Type:
    Media
    Description/Abstract:
    This webinar aired on January 12, 2016 for members of the DataCure listserv. The webinar covered issues around sensitive data and how to establish a educational program to help researchers protect sensitive data while sharing results of their research. The presenters were Brett Harnett, Director of the UC Center for Health Informatics ( http://www.med.uc.edu/chi) and Jonathan Petters Ph.D. Data Management Consultant at Johns Hopkins University ( http://dmp.data.jhu.edu/). Presentation 1 - Brett Harnett- the process of de-identifying data especially data resulting from medical records, issues around de-identifying especially unstructured data, working with an IRB and future issues concerning data containing PHI. Presentation 2 - Jonathan Petters - training for de-identifying human subjects data for sharing and developing a viable library service.
    Creator/Author:
    Koshoffer, Amy; Harnett, Brett, and Petters, Jonathan
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    03/09/2016
    Date Modified:
    11/18/2016
    Date Created:
    2016-03-09
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • Table 3 IDCC2018 Detailed Metadata Comparison

    Document thumbnail: Table 3 IDCC2018 Detailed Metadata Comparison
    Type:
    Document
    Description/Abstract:
    This table compares metadata elements for data sets in four institutional repositories and includes the Dublin Core metadata elements they map to.
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    03/22/2018
    Date Modified:
    05/31/2018
    Date Created:
    2017-10-21
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • Publication equality in conservation and plant genetics: The impact of neocolonialism in modern science

    Image thumbnail: Publication equality in conservation and plant genetics: The impact of neocolonialism in modern science
    Type:
    Image
    Description/Abstract:
    This Poster describes a collaborative research project between the Culley and Tepe labs in the UC Department of Biology and UC Libraries Digital Scholarship Center presented at the 2017 UC Data Day ( https://libapps.libraries.uc.edu/blogs/dataday/past-data-days/). The project explores publication patterns of research involving hotspot areas of biodiversity and if researchers from developing countries which tend to have most of the biodiversity hotspots, are adequately represented as authors in the scientific literature indexed in Scopus (TM-Elsevier), JSTOR, and PubMed.
    Creator/Author:
    Koshoffer, Amy; Torres, Maria; Merritt, Benjamin; Barreiro-Sanchez, José; Johnson, Arlene; Tunison, Robert; Ammar, Marwa; Tepe, Eric; Elam, Robert; Philpott, Megan; Culley, Theresa, and Lee, James
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    10/27/2018
    Date Modified:
    05/23/2019
    Date Created:
    2017-03
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • Table 2 IDCC2018 Required and Optional Metadata Comparison

    Document thumbnail: Table 2 IDCC2018 Required and Optional Metadata Comparison
    Type:
    Document
    Description/Abstract:
    This figure compares optional and required metadata fields for data sets for four institutional repositories -UC, U Michigan, U Minnesota, and Oregon State
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    03/22/2018
    Date Modified:
    05/15/2018
    Date Created:
    2017-10-21
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • Table 1 IDCC 2018 Institutional Repository Comparison

    Dataset thumbnail: Table 1 IDCC 2018 Institutional Repository Comparison
    Type:
    Dataset
    Description/Abstract:
    This table compares the major characteristics of the four sources repositories in in the metadata study
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    07/03/2018
    Date Modified:
    07/03/2018
    Date Created:
    License:
    Open Data Commons Attribution License (ODC-By)

  • Figure 1 IDCC 2018 Percent Completeness of Data Set Metadata

    Dataset thumbnail: Figure 1 IDCC 2018 Percent Completeness of Data Set Metadata
    Type:
    Dataset
    Description/Abstract:
    This data set describes the percent completeness of metadata options for data sets in four institutional repositories. It is a derivative data set from the master data set entitled "Metadata of data sets from four institutional repositories"  https://scholar.uc.edu/show/pn89d657h Please use the Citation: Koshoffer, A., Neeser, A., Johnston L.R., and Newman L.D., (2018) "Giving datasets context: a comparison study of institutional repositories that apply varying degrees of curation", International Digital Curation Conference, Barcelona, Spain. Digital Curation Centre.
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    01/12/2018
    Date Modified:
    10/27/2018
    Date Created:
    2017-09
    License:
    Open Data Commons Attribution License (ODC-By)

  • Master Data set - Metadata of data sets from four institutional repositories

    Dataset thumbnail: Master Data set - Metadata of data sets from four institutional repositories
    Type:
    Dataset
    Description/Abstract:
    This data set is the raw data underlying the paper entitled "Giving datasets context: a comparison study of institutional repositories that apply varying degrees of curation" presented at the International Digital Curation Conference in Barcelona, Spain (Feb 2018). All figures and tables in the publication were based on the analysis of this data set. Please use the Citation: Koshoffer, A., Neeser, A., Johnston L.R., and Newman L.D., (2018) "Giving datasets context: a comparison study of institutional repositories that apply varying degrees of curation", International Digital Curation Conference, Barcelona, Spain. Digital Curation Centre.
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    01/11/2018
    Date Modified:
    10/27/2018
    Date Created:
    2016-06 to 2017-10
    License:
    Open Data Commons Attribution License (ODC-By)

  • Figure 2 IDCC2018 Documentation for data sets in four institutional repositories

    Dataset thumbnail: Figure 2 IDCC2018 Documentation for data sets in four institutional repositories
    Type:
    Dataset
    Description/Abstract:
    This data set describes the type and number of documentation that accompany and describe data sets in four institutional repositories. It is a derivative data set from the master data set entitled "Metadata of data sets from four institutional repositories"  https://scholar.uc.edu/show/pn89d657h
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    01/12/2018
    Date Modified:
    05/15/2018
    Date Created:
    2017-09
    License:
    Open Data Commons Attribution License (ODC-By)

  • Figure 4 IDCC2018 Keywords data sets four institutional repositories

    Dataset thumbnail: Figure 4 IDCC2018 Keywords data sets four institutional repositories
    Type:
    Dataset
    Description/Abstract:
    This data set measures the number of keywords associated with data sets in four institutional repositories. It is a derivative data set from the master data set entitled "Metadata of data sets from four institutional repositories"  https://scholar.uc.edu/show/pn89d657h
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    01/12/2018
    Date Modified:
    05/15/2018
    Date Created:
    2017-09
    License:
    Open Data Commons Attribution License (ODC-By)

  • Figure 1 IDCC 2018 Significance analysis metadata study four institutional repositories

    Dataset thumbnail: Figure 1 IDCC 2018 Significance analysis metadata study four institutional repositories
    Type:
    Dataset
    Description/Abstract:
    This data set describes the Mann-Whitney U test statistical analysis of the completeness profile for data sets in four institutional repositories. It is a derivative data set from the master data set entitled "Metadata of data sets from four institutional repositories"  https://scholar.uc.edu/show/pn89d657h
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    01/12/2018
    Date Modified:
    05/15/2018
    Date Created:
    2017-09
    License:
    Open Data Commons Attribution License (ODC-By)

  • Figure 3 IDCC 2018 Digital object identifiers for data sets in four institutional repositories

    Dataset thumbnail: Figure 3 IDCC 2018 Digital object identifiers for data sets in four institutional repositories
    Type:
    Dataset
    Description/Abstract:
    This data set measures whether data sets in four institutional repositories have digital object identifiers or not. It is a derivative data set from the master data set entitled "Metadata of data sets from four institutional repositories"  https://scholar.uc.edu/show/pn89d657h
    Creator/Author:
    Koshoffer, Amy; Newman, Linda; Neeser, Amy, and Johnston, Lisa
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    01/12/2018
    Date Modified:
    05/15/2018
    Date Created:
    2017-09
    License:
    Open Data Commons Attribution License (ODC-By)

  • Ultrastructural Heterogeneity in Melanocytes Cultured from Patients with Chediak-Higashi Syndrome

    Generic Work thumbnail: Ultrastructural Heterogeneity in Melanocytes Cultured from Patients with Chediak-Higashi Syndrome
    Type:
    Generic Work
    Description/Abstract:
    Koshoffer, Amy
    Creator/Author:
    Koshoffer, Amy; Westbroek, Wendy; Gahl, William A.; Boissy, Raymond E., and Huizing, Marjan
    Submitter:
    Amy Koshoffer
    Date Uploaded:
    12/12/2016
    Date Modified:
    02/06/2017
    Date Created:
    2006-08-31
    License:
    All rights reserved