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Archive for the ‘Data’ Category

Demonstrating Your Impact: Collecting Stories

Monday, August 19th, 2013

To demonstrate your library’s impact to decision makers, it can be helpful to bring your data to life with some great success stories: researchers that were helped by your librarians, doctors’ time saved, or patients understanding their follow-up instructions.  Even better than success stories you tell your administration are stories told about your library by satisfied customers, for example, satisfied doctors whose time is valuable to their hospital as well as themselves, satisfied patients who can recommend your hospital to others, or satisfied researchers who can vote where their city dollars go. In addition, there is evidence that anecdotal data can influence the outcomes of decisions (

Native American woman working with her daughterPart 2 in the Demonstrating Your Impact series is about collecting and telling those success stories.  The Centers for Disease Control and Prevention (CDC) has a publication called Impact and Value: Telling Your Program’s Story document is intended for program managers to provide steps they can use to systematically collect and create success stories: “with attention to detail, a system of regular data collection and practice, this tool can become a powerful instrument to spread the word about your program.”

According to the Impact and Value publication, stories should not be the main method of presenting data, but they put a face to the numbers of research and evaluation data: “What does it really mean when you report that you have provided ‘X’ amount of services to ‘Y’ amount of people? How are the lives of the program participants [or your library customers] changed because of your services?”


A great example of systematic story collection can be found in the article, “MedlinePlus and the challenge of low health literacy: findings from the Colonias project,” ( which describes a project funded by the National Library of Medicine in which community health workers, known as promotoras, were trained to help members of some Texas-Mexico border communities find health information using MedlinePlus. These promotoras were asked to collect up to two stories every week on how they used online resources to help residents with health concerns.  The 157 stories that resulted from this technique were treated as data: thematically coded, checked for validity, and studied to show the degree of success of the promotoras project.

What to do with all this data? Stay tuned for part 3 of the Demonstrating Your Impact series: Telling Your Story


Demonstrating Your Impact: Return on Investment

Tuesday, August 6th, 2013

Graph of Cost and Benefit with Push Pin near benefit

Are you looking for ways to demonstrate your impact to your administration? This is Part 1 in a 3 part series on demonstrating your impact.

The NN/LM MidContinental Region (MCR) has created three online tools that can be used to enable a library to put actual figures to their importance within an organization

CBA/ROI Calculator: Sometimes it’s a good idea to speak the language of the administration. Cost/Benefit Analysis (CBA) and Return on Investment (ROI) are measures used by financial managers to indicate if their money is going in the right place.  In a cost/benefit analysis, the goal is to show how much benefit the organization receives for the cost of the library.  For a cost/benefit analysis, the result is actually a number: the benefit to cost ratio.  If $50 was spent and the benefits to the organization could be seen as worth $25, then the benefit/cost ratio would be $25/$50 or 1/2 (50 cents of benefit for every $1 spent). This would obviously not look good for your library.  However, if you could show that for the $50 that was spent, the benefit to the organization could be valued at $150, then the benefit/cost ratio is $150/$50, or $3 of benefit for every $1 spent.  This could make your library look like a real asset!

Return on Investment is a very similar concept.  In order to get the final percentage, the benefit of an investment (minus the original cost) is divided by the cost of the investment.  So using the figures from the second case above, if $50 had been spent and a $150 benefit was achieved, $50 is subtracted from $150 to show a total return of $100.  Then dividing that by the original investment ($100/$50), equals 2.00 or 200%.  A 200% return on investment would make your library look very good!

“How can I apply this to my library” you might ask?  The CBA/ROI Calculator from the MidContinental Region does most of the work for you.  You simply fill in the blanks with the cost of books, cost of staff time, time saved, etc., and the final costs, benefits and ratios are determined at the bottom.

Database ROI Calculator: The calculator above is mostly designed for the books in your library’s collection.  The MCR also provides a CBA/ROI calculator for databases.  Getting statistics for databases can be a little more difficult than for other library services. Databases are often bundled with other products, and vendors define use statistics in multiple ways that make if difficult to compare across databases.  Nevertheless, the MidContinental Region has some helpful tips for deciding which statistics to enter.

Valuing Library Services Calculator: Isn’t this what we all want – to explain that our library services have a financial value to the organization?  Using this calculator, you can assign a dollar amount to the services you supply based on their retail value.  You type in the number of times a particular service is used, and the calculator multiplies it by the retail value of that service. And at the bottom, it sums up your library’s total retail value.

The MCR is gathering data for advocacy purposes. If you would like your data included, be sure to fill out the form completely including the CAPTCHA box, and hit “submit data.” Librarians everywhere will appreciate your thoughtfulness.

Stay tuned for Part II – Collecting Stories.

NIH Commits Funding for Big Data Centers of Excellence

Thursday, July 25th, 2013

Big Data Road Sign

The National Institutes of Health (NIH) recently announced plans to fund up to $24 million per year for four years to establish six to eight investigator-initiated Big Data to Knowledge Centers of Excellence. The centers will improve the ability of the research community to use increasingly large and complex datasets through the development and distribution of innovative approaches, methods, software, and tools for data sharing, integration, analysis and management. The centers will also provide training for students and researchers to use and develop data science methods.

Biomedical research is increasingly data-intensive, with researchers routinely generating and using large, diverse datasets. Yet the ability to manage, integrate and analyze such data, and to locate and use data generated by others, is often limited due to a lack of tools, accessibility, and training. In response, NIH launched the Big Data to Knowledge (BD2K) initiative in December. This initiative supports research, implementation, and training in data science that will enable biomedical scientists to capitalize on the transformative opportunities that large datasets provide. The investigator-initiated BD2K Center of Excellence funding opportunity is the first of several BD2K funding opportunities to be announced in coming months.

An information webinar for prospective applicants will be held on Thursday, Sept. 12, 2013, from 3 p.m. to 5 p.m. EDT. More details about this event and the overall BD2K initiative can be found at NIH Big Data to Knowledge (BD2K) website. Applications will be due on Nov. 20, 2013.

Big Data to Knowledge BD2K

Tuesday, July 23rd, 2013

The National Institutes of Health (NIH) Commonfund recently launched the Big Data to Knowledge (BD2K) imitative. The mission of the BD2K initiative is to enable biomedical scientists to capitalize more fully on the Big Data being generated by those research communities.

Big Data Tablet

With advances in technologies, these investigators are increasingly generating and using large, complex, and diverse datasets. Consequently, the biomedical research enterprise is increasingly becoming data-intensive and data-driven. However, the ability of researchers to locate, analyze, and use Big Data (and more generally all biomedical and behavioral data) is often limited for reasons related to access to relevant software and tools, expertise, and other factors. BD2K aims to develop the new approaches, standards, methods, tools, software, and competencies that will enhance the use of biomedical Big Data by supporting research, implementation, and training in data science and other relevant fields that will lead to:

  • Appropriate access to shareable biomedical data through technologies, approaches, and policies that enable and facilitate widespread data sharing, discoverability, management, curation, and meaningful re-use;
  • Development of and access to appropriate algorithms, methods, software, and tools for all aspects of the use of Big Data, including data processing, storage, analysis, integration, and visualization;
  • Appropriate protections for privacy and intellectual property;
  • Development of a sufficient cadre of researchers skilled in the science of Big Data, in addition to elevating general competencies in data usage and analysis across the biomedical research workforce.

Overall, the focus of the BD2K initiative is the development of innovative and transforming approaches as well as tools for making Big Data and data science a more prominent component of biomedical research.

As biomedical tools and technologies rapidly improve, researchers are producing and analyzing an ever-expanding amount of complex biological data. New analytics tools are needed to extract critical knowledge from this vast amount of data, and new policies must be developed to encourage data and software sharing to maximize the value of the data for all researchers across the spectrum of biomedical research. In addition, data and metadata standards to ensure data quality and uniformity must be developed, with broad input from the scientific community to ensure that these standards will have maximum utility and value.

Funding and educational opportunities are provided through the BD2K initiative.

Each day more and more data is generated. Through efforts such as the BD2K initiative it is hoped that the data can be widely used across disciplines  and lead to scientific discovery or breakthroughs, particularity in the fields of health and medicine. Health science librarians also play an important role in the organization and curation of data. With expert skills in organization of information librarians are well suited to participate with researchers in data organization processes.

Funny and Informative Data Sharing and Management Video

Tuesday, July 16th, 2013

Librarians at New York University’s Health Sciences Libraries (NYUHSL) developed a series of short videos to highlight the importance of data sharing under Federally funded awards and grants. The Librarians at NYUHSL developed the videos with funding from “information specialist” supplements to existing National Institutes of Health (NIH) research grants. The National Library of Medicine (NLM) initiated these supplements as a pilot last year. They were offered by several NIH Institutes and Centers.

The NYUHSL librarians developed three short videos using Xtranormal animation and later combined them into one. The videos, while funny, highlight the importance of data sharing and the unfortunately funny situations that can arise when data is not managed correctly.

As data sets become an increasingly important aspect of continued research across departments and divisions researchers may need assistance understanding data management policies and data sharing. Increasingly librarians have demonstrated that they can play an important role in helping researchers with the data management process and data management policy creation.

NIH Data Sharing Repositories

Wednesday, June 19th, 2013

Screenshot of NIH Data Sharing Repository

The National Institutes of Health (NIH) recently made available a list of data sharing repositories. The NIH Data Sharing Repositories is a searchable list of NIH-supported data repositories that accept submissions of appropriate data from NIH-funded investigators (and others). Also included are resources that aggregate information about biomedical data and information sharing systems.

Also available, NIH Data Sharing Policies, provide a list of data sharing policies in effect at the NIH, including policies at the NIH, IC, division, and program levels that apply to broad sets of investigators and data. Individual requests for applications (RFAs) and program announcements (PA) may specify other data sharing policies for specific projects.

The data repositories and sharing policies provided by the NIH are the work of the NIH Trans-NIH BioMedical Informatics Coordinating Committee (BMIC) which was established in the Spring of 2007 to improve communication and coordination of issues related to clinical- and bio-informatics at NIH. The Committee provides a forum for sharing information about NIH informatics programs, projects, and plans, including their relationship to activities of other federal agencies and non-government organizations.

Data sharing is becoming an important aspect of scientific research with benefits that include:

  • reinforcing open scientific inquiry,
  • encouraging diversity of analysis and opinion,
  • promoting new research, testing of new or alternative hypotheses and methods of analysis,
  • supporting studies on data collection methods and measurement,
  • facilitating education of new researchers,
  • enabling the exploration of topics not envisioned by the initial investigators,
  • permitting the creation of new datasets by combining data from multiple sources.

Benefits are not just limited to the scientific community. With data sharing everyone benefits, including investigators, funding agencies, the scientific community, and, most importantly, the public. Data sharing provides more effective use of NIH resources by avoiding unnecessary duplication of data collection. It also conserves research funds to support more investigators. The initial investigator benefits, because as the data are used and published more broadly, the initial investigator’s reputation grows.