Francine Berman

Got data?: a guide to data preservation in the information age

Tools for surviving a data deluge to ensure your data will be there when you need it.

Who Will Pay for Public Access to Research Data

When economic models and infrastructure are not in place to ensure access and preservation, federally funded research data are “at risk.” On 22 February, the U.S. Office of Science and Technology Policy (OSTP) released a memo calling for public access for publications and data resulting from federally sponsored research grants (1). The memo directed federal agencies with more than

Social and ethical behavior in the internet of things

100 million R&D expenditures to “develop a plan to support increased public access to the results of research funded by the Federal Government.” Perhaps even more succinctly, a subsequent New York Times opinion page sported the headline “We Paid for the Research, So Lets See It” (2). So who pays for data infrastructure?

Realizing the potential of data science

interconnected devices infected with malware mounted a denial-of-service cy-berattack on Dyn, a company that operates part of the Internets directory service. Such attacks require us to up our technical game in Internet security and safety. They also expose the need to frame and enforce social and ethical behavior, privacy, and appropriate use in Internet environments. Social behavior and appropriate use become even more crucial as we build out the Internet of Things (IoT)—an increasingly interconnected cyber-physical-biological environment that links devices, systems, data, and people. At its best, the IoT has the potential to create an integrated ecosystem that can respond to a spectrum of needs, increasing efficiency and opportunity, and empowering people through technology , and technology through intelligence. At its worst, the IoT can open a Pandoras Box of inappropriate and unsafe behavior, unintended consequences , and intrusiveness. The difference between an IoT that enhances society and one that diminishes it will be determined by our ability to create an effective model for IoT governance. This model must guide social behavior and ethical use of IoT technologies while promoting effective security and safety. While we should not limit technology innovation too early with overly restrictive policy, neither should we leave the policy and governance discussion until the IoT is so mature that it cannot easily incorporate pro-tections. What Policy Will Be Needed for the IoT? Although much of the policy needed for the IoT may evolve from Internet gov-ernance, the scale, heterogeneity, complexity , and degree of technological autonomy within the IoT will require new thinking about regulation and policy and force new interpretations of current law. As an example of the complexity of the governance challenge, consider three key areas critical to ensure the positive potential of the IoT: 1. What are your rights to privacy in the IoT? The IoT will sharpen the tension between individual privacy and the use of personal information to promote effectiveness, safety, and security. Who should control information about you? Who should access it? Who can use it? The answer is not always clear-cut. Consider medical monitoring devices and the information they accumulate. Should your personal health information be shared when the Centers for Disease Control want to track a potential epidemic? When bio-medical researchers want to model potential treatment strategies on a richer dataset? When an employer is considering you for a job? At present, policy and …

Let's Make Gender Diversity in Data Science a Priority Right from the Start

Data science promises new insights, helping transform information into knowledge that can drive science and industry.

We need a research data census

The emergent field of data science is a critical driver for innovation in all sectors, a focus of tremendous workforce development, and an area of increasing importance within science, technology, engineering, and math (STEM). In all of its aspects, data science has the potential to narrow the gender gap and set a new bar for inclusion. To evolve data science in a way that promotes gender diversity, we must address two challenges: (1) how to increase the number of women acquiring skills and working in data science and (2) how to evolve organizations and professional cultures to better retain and advance women in data science. Everyone can contribute.

What Do We Know About The Stewardship Gap

The increasing volume of research data highlights the need for reliable, cost-effective data storage and preservation at the national scale.

From TeraGrid to Knowledge Grid.

In the 21st century, digital data drive innovation and decision-making in nearly every field. However, little is known about the total size, characteristics, and sustainability of these data. In the scholarly sphere, it is widely suspected that there is a gap between the amount of valuable digital data that is produced and the amount that is effectively stewarded and made accessible. The Stewardship Gap Project (http://bit.ly/stewardshipgap) investigates characteristics of, and measures, the stewardship gap for sponsored scholarly activity in the United States. This paper presents a preliminary definition of the stewardship gap based on a review of relevant literature and investigates areas of the stewardship gap for which metrics have been developed and measurements made, and where work to measure the stewardship gap is yet to be done. The main findings presented are 1) there is not one stewardship gap but rather multiple gaps that contribute to whether data is responsibly stewarded; 2) there are relationships between the gaps that can be used to guide strategies for addressing the various stewardship gaps; and 3) there are imbalances in the types and depths of studies that have been conducted to measure the stewardship gap.