The Value & Impact of Data Sharing & Curation

Jisc has published the synthesis report of the value & impact studies of Economic and Social Data Service (ESDS), the Archaeology Data Service (ADS), and the British Atmospheric Data Centre (BADC). This report summarizes and reflects on the findings from a series of recent studies, conducted by Neil Beagrie of Charles Beagrie Ltd. and Prof. John Houghton of Victoria University, into the value and impact of these three well established research data centers . It provides a summary of the key findings from new research and reflects on: the methods that can be used to collect data for such studies; the analytical methods that can be used to explore value, impacts, costs and benefits; and the lessons learnt and recommendations arising from the series of studies as a whole.

Research Data Needs Assessment Study by the University of Iowa

The data management report, authored by Shawn Averkamp, Xiaomei Gu, and Ben Rogers, presents survey and interview findings from their recent data needs assessment study. This report was commissioned by the University of Iowa Libraries with the intention of performing a survey of the campus landscape and identifying gaps in data management services. The first stage of data collection consisted of a survey conducted during summer 2012 to which 784 responses were received. The second phase of data collection consisted of approximately 40 in-depth interviews with individuals from the campus and were completed during summer 2013. Findings are presented within five broad areas of data management: 1) data management planning, 2) data storage, 3) data organization and analysis, 4) data publishing and dissemination and, 5) sensitive data and compliance, with additional findings reported in the areas of research culture and funding models.

A Short Guide to Better Manage Scientific Data

10 Simple Rules for the Care and Feeding of Scientific Data 

In this article entitled 10 Simple Rules for the Care and Feeding of Scientific Data, authors from Computational Biology and Information Science (Alyssa Goodman, Alberto Pepe, Alexander W. Blocker, Christine L. Borgman, Kyle Cranmer, Mercè Crosas, Rosanne Di Stefano, Yolanda Gil, Paul Groth, Margaret Hedstrom, David W. Hogg, Vinay Kashyap, Ashish Mahabal, Aneta Siemiginowska, Aleksandra Slavkovic) offer a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have been written on questions of data sharing, data provenance, research reproducibility, licensing, attribution, privacy, and more, but the goal of this article is not to review that literature. Instead, authors present a short guide intended for researchers who want to know why it is important to “care for and feed” data, with some practical advice on how to do that.

Libraries’ New Role in Research Data Management: The Case of Denmark

Academic libraries and RDM: Current trends and visions in Denmark

Research libraries’ new role in research data management has been widely discussed around the world. In the following article in LIBER Quarterly, F. Kruse and J. B. Thestrup discuss current trends and visions in Denmark.

This paper presents the findings of a research project carried out under the auspices of DEFF (Danmarks Elektroniske Fag- og Forskningsbibliotek — Denmark’s Electronic Research Library) to analyse how the Danish universities store, preserve and provide access to research data. It shows that they do not have a common IT-infrastructure for research data management. This paper describes the various paths chosen by individual universities and research institutions, and the background for their strategies of research data management. Among the main reasons for the uneven practices are the lack of a national policy in this field, the different scientific traditions and cultures and the differences in the use and organization of IT-services.

You can find a full text of this article here.

Starting the Conversation: University-wide Research Data Management Policy

EDUCAUSE Review on Data Management Policy 

This article represents a call for action to address the high-level benefits of adopting a university-wide policy regarding research data management. An institution should identify all the stakeholders, bring them together to discuss their interests, create policy, and actively determine how it will manage the university’s data assets.

Suggested elements of the conversation include:

  • Who owns the data?
  • What Requirements are Imposed By Others?
  • Which Data Should Be Retained?
  • For How Long Should Data Be Maintained?
  • How Should Digital Data Be Preserved?
  • Are there Ethical Considerations?
  • How are Data Accessed?
  • How Open Should the Data Be?
  • How Will Costs Be Managed?
  • What are the Alternatives to Local Data Management?

Educause Review Nov/Dec 2013

A full report can be downloaded from the OCLC website.

What Is a Data Paper? | CDL

What’s a data paper you ask?

The report titled PRACTICES, TRENDS,
 AND
 RECOMMENDATIONS
 IN
 TECHNICAL
 APPENDIX
 USAGE
 FOR
 SELECTED
 DATA‐INTENSIVE 
DISCIPLINES by John
 Kunze,
 Trisha
 Cruse,
 Rachael
 Hu, Stephen 
Abrams, 
Kirk
 Hastings,
 Catherine
 Mitchell,
 and Lisa
 Schiff will help you answer the question.

You can find the report here : http://www.cdlib.org/services/uc3/docs/dax.pdf

Visualization: The Simple Way to Simplify Big Data | WIRED

Visualization: The Simple Way to Simplify Big Data

Data visualization has an amazing ability to make the complex simple, and the latest tools can do much more than give everyone the same view of data. It’s only through visualization that we can take something as abstract as symbols and turn it into a physical image that has dimensions that our eyes can quickly see and our brains understand. We can grasp data’s meaning more quickly. Visualization tells us when trends are heading in the wrong direction and we need to intervene.

A full article is available here: http://www.wired.com/insights/2013/08/visualization-the-simple-way-to-simplify-big-data/