The Data Curation Profiles Toolkit: The Profile Template

The Data Curation Profile is composed of the following sections and sub-sections, each of which is defined below. The information used to populate the profile will largely come from the information gathered through the use of the Interview Worksheet and the Interviewer’s Manual. The following table provides a broad overview of how the interview modules correspond to the sections of the Data Curation Profile template

Data Curation Format Profile: MATLAB

A primer for the MATLAB (*.mat and *.m) file formatw for the purpose of data curation. It covers various tools for viewing MATLAB files and questions to consider when curating a MATLAB dataset or file.https://deepblue.lib.umich.edu/bitstream/2027.42/154686/1/Data Curation Format Profile - MATLAB.pd

Data Curation Format Profile: netCDF

A primer for the netCDF (*.nc) file format for the purpose of data curation. It covers various tools for viewing netCDF files and questions to consider when curating a netCDF dataset or file.https://deepblue.lib.umich.edu/bitstream/2027.42/145724/1/Public-Data-CurationFormat Profile-netCDF.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145724/5/Public-Data-CurationFormat Profile-netCDFv2.pdfDescription of Public-Data-CurationFormat Profile-netCDF.pdf : Original versionDescription of Public-Data-CurationFormat Profile-netCDFv2.pdf : Version 2.0 with Integrated Data Viewer information from Sophie Ho

Creating Data Profiles

This presentation gives an overview of how data profiles can be created by using Purdue University's Data Curation Profiles Toolkit. The presentation touches on what a data curation profile is, how it is developed, the purpose and use for it, and the different components of the Toolkit. It also gives an overview on the User Guide, the Interviewer's Manual, the Interview Worksheet, and the Data Curation Profile Template. Lastly it shows how data curation profiles can be uploaded on the Data Curation Profiles Directory.Presentation delivered at the NeDICC Workshop on 'The role of the information professional in relation to Research Data Management', CSIR, Pretoria, South Africa, 22 June 201

Data Management and Sharing from the Perspective of Graduate Students: An Examination of Culture and Practice at the Water Quality Field Station

Libraries are actively seeking to identify and respond to the data management and curation needs of researchers. One important perspective in this area that is often overlooked is that of graduate students. This case study uses the Data Curation Profile Toolkit to interview six graduate students working for Agronomy researchers at the Water Quality Field Station (WQFS) research lab to understand the students’ practices with data, the challenges they face, and their attitudes towards managing and sharing data. Though a small study, this research could provide new insights for libraries creating data services, particularly in regards to graduate students

Participatory Prototype Design: Developing a Sustainable Metadata Curation Workflow for Maternal Child Health Research

This paper describes the findings from a participatory prototype design project, where the authors worked with maternal and child health (MCH) researchers and stakeholders to develop a MCH metadata profile and sustainable curation workflow. This work led to the development of three prototypes: 1) a study catalogue hosted in Dataverse, 2) a metadata and research records repository hosted in REDCap and 3) a metadata harvesting tool/dashboard hosted within the Shiny RStudio environment. We present a brief overview of the methods used to develop the metadata profile, curation workflow and prototypes. Researchers and other stakeholders were participant-collaborators throughout the project. The participatory process involved a number of steps, including but not limited to: initial project design and grant writing; scoping and mapping existing practices, workflows and relevant metadata standards; creating the metadata profile; developing semi-automated and manual techniques to harvest and transform metadata; and end project sustainability/future planning. In this paper, we discuss the design process and project outcomes, limitations and benefits of the approach, and implications for researcher-oriented metadata and data curation initiatives

A Letter from Your Library Liaisons

New Opt-in Library Approval for Monographs Help Build the Library Collections New Library Faculty Profile: Jeff Mortimore, Discovery Services and Data Curation Librarianhttps://digitalcommons.georgiasouthern.edu/liaisons/1002/thumbnail.jp

Opportunities and Barriers for Librarians in Exploring Data: Observations from the Data Curation Profile Workshops

Setting and Objective: The Purdue University Libraries offered a series of professional development workshops in 2011-2012 to provide librarians with a broad understanding of issues in data curation and to train them in the use of the Data Curation Profiles Toolkit (DCP Toolkit). An additional goal of the workshop was to develop a better understanding of the experiences, attitudes, and needs of librarians as they explore new roles. Design and Methods: Workshop participants were asked to complete three surveys: one before the workshop, one right afterwards, and one delivered three months after they had attended the workshop. Participants’ responses to the survey questions that pertained to demographic information, confidence levels, and levels of engagement before and after the workshop were reviewed and analyzed. Results: The results of the survey indicated that participants’ confidence levels in multiple areas increased after the workshop, but that their levels of engagement remained relatively stagnant. An analysis of the free text comments made in the survey revealed multiple issues in librarians’ efforts to increase their engagement in working with data including time, staffing, and organizational support from their library. Conclusions: The challenges encountered by librarians seeking to engage in data management and curation issues are found at the individual level (acquiring skills and confidence) and at the organizational level (creating a supportive environment). Both levels will need to be addressed by libraries seeking to develop data services

Rapid Development of Clinical Trial Candidates Using Cancer Systems Pharmacology: a Lymphoma Case Study

poster abstractDue to intrinsic complex molecular interactions, the “one disease – one target – one drug” strategy for disease treatment is no longer the best option to treat complex diseases such as cancers. To assess drug pharmacological effects, we assume that “ideal” drugs for patients can treat or prevent the disease by modulating its gene expression profile to a similar level of those in healthy people. A drug that may not have been approved to treat a cancer yet, based on its gene expression target profile is the most successfully at modulating the gene expression to being of similar level to a healthy person is known as drug repurposing. The goal of this study was to develop an in silico framework which would determine which drug(s) could be repurposed to treat more complex disease of interest such as cancers. Using three subcategories of Non-Hodgkin’s Lymphoma (Burkitt’s, Mantle, Diffuse Large B-Cell) as case studies, manual curation was done to collect data on drug-protein interaction, drug similarity analysis based on structure and protein target, and curation; disease-protein interactions, and protein-protein interactions. A network will be created from the curated data known as a Pharmacology Effect Network (PEN). The Pharmacological Effect on Target (PET) algorithm will then be used to rank the curated drugs. This ranking will help determine which of the investigated drugs not currently used to treat one of the three subsets of Non-Hodgkin’s lymphoma could possibly be recommended to treat them. Although this project was primarily done using manual curation, the framework of each curated relationship used by each curator has been incorporated into a web interface. This webpage will allow for more automation of the curation process with little help from the curator and should improve the speed and accuracy of the curation process. Mentors: Jake Chen7, Xiaogang Wu7, Walter Jessen8 7IU Center for Systems Biology and Personalized Medicine, IUPUI; 8Informatics, Covance, Greenfiel

Biophysics - Cornell University

This data curation profile focuses on a data set generated by a researcher studying the physical interactions of proteins. Specifically, he used small angle x-ray scattering in order to examine how proteins respond to a light stimulus. The data management needs involve making available and linking the refined data to publications. There are some concerns about making the raw data available due to the potential for misinterpretation and also the effort require to make it available