Archive | Computers in Libraries

Jennifer Fagan-Fry

Jennifer Fagan-Fry, NOAA IR Manager, NOAA Central and Regional Libraries, said that NOAA is part of the Department of Commerce, and its research covers from the bottom of the ocean to the surface of the sun. The central library is a network of over 20 libraries across the country. It collects current and historical NOAA publications and data and maintains the collection inherited from NOAA’s predecessor agency. The NOAA IR includes over 18,000 NOAA authored publications and sponsored ones, and grey literature (Technical Reports and Memoranda) dating from 1970 to the present. The IR is open to the public. NOAA has an IR to comply with the 2003 White House Office of Science and Technology policy memo that mandated public access to research.

Before the IR: NOAA was formed in 1970 and consisted of 6 different areas of research. The research was organized into “Silos of Excellence”. There was a lack of agency-wide policies and procedures and little communication between the offices. Access points were isolated. Publications were scattered across the web: on journal publisher sites, on NOAA office and program sites, in university IRs, and on social networking sites.

After the IR was established, it became the primary place for NOAA publications. OA publishing increased and is now a requirement for some grants. There is an easier submission process to the IR so people are encouraged to publish in OA whenever possible. There is a movement towards standardized publishing process. The IR is designed to provide long term access and preservation of research. Communication between offices has improved. Data sets have been linked as have been publications, R&D projects and outcomes, and grant information.  Coordination between offices for submission has improved so duplication has decreased.

The library’s role: increased profile and services. It has become more vocal and is doing more outreach. Anything in the IR is not in the library catalog which has caused a shift in cataloging practices. The IR is indexed by Google, but the catalog is not.

The biggest change for the library and IR is enforcement of Section 508 which requires electronic information and technologies to be available for people with disabilities. It was not enforced at NOAA prior to the IR. Now, all submissions to the IR go through 508 quality checks, which caused a lot of resistance and a drop in submissions. A LibGuide was created, and submissions increased and have exceeded previous levels. This was the biggest cultural shift in the agency.

Ongoing challenges:

• Getting buy in from stakeholders, authors, etc. and creating an enforcement mechanism for submissions or participation.
• Resources: tools, staff to process and manage submissions.

Going forward, the library will become the publishing clearninghouse for NOAA, data sharing, ORCID integration, and internal reporting tools will be established.

Brian Pichman

Brian Pichman, Director of the Evolve Project, said that AI is the theory and development of computer systems able to perform tasks that normally require human intelligence; it is becoming more available to the masses. Machine learning is a subset of AI that uses algorithms and statistical models to allow a computer system to make decisions around a task without explicit instructions, relying on patterns and inference.

AI has 4 components: it is logic-based, rules-based, pattern-based, and includes deep learning and neural networks. It exists with self-driving cars, etc. (pattern-based); Strong AI does not exist yet.  Pattern recognition means to create an algorithm able to teach itself without any external help. Voice recognition systems can respond and remember a user based solely on their voice.  Samsung has released bots to monitor air quality, place orders, etc. We use AI for optical character recognition, to advance user preferences (recommendations), and sensory data analysis.

AI is used in healthcare to identify and notice predictable trends, read X-rays, and gather information. Here is a good article on possible manipulations.

Other applications:

• Smart homes can issue an alert to distinguish a person vs. an animal, know that it will rain tomorrow so there is no need to water the lawn today. Robots are coming out for home use.
• AI has the potential to replace many minimum wage jobs, especially in the service industries. There is already a burger-flipping bot.
• ROSS can make legal research faster and easier.

Challenges include precision, context, and training. It is important to make sure that the data provided is correct and useful.

AI in libraries:  There are many skills to learn. AI can do simple tasks so you can focus on other things. There is some impact on staff. How do we handle simple reference questions? We can make jobs more meaningful and get rid of tedious work.

AI does not have emotional intelligence and cannot show empathy; decisions are based on facts.

Mark Eaton and Robin Camille Davis

Mark Eaton from Kingsborough Community College, CUNY, and Robin Camille Davis, John Jay College of Criminal Justice, CUNY, described a workshop they developed to teach librarians how to make Twitter bots. They began with first principles:

• A bot is a little program that does the same thing over and over again, with a pre-programmed variation.
• A Twitter bot usually sends out a thematic tweet on a regular basis. The tweet is created using a script and some data.
• Manipulative bots don’t identify themselves and retweet a political slogan over and over. 

The workshop emphasized becoming familiar with the Python programming language. It was presented three times at conferences. There are many claims that coding is a new literacy; today the definition has expanded from traditional notions of reading and writing to the ability to learn, comprehend,and interact with technology in a meaningful way. In their workshop, they wanted participants to learn how to build bots, what tools are needed to learn to make bots, how to deploy a bot, and the social implications of bot making.

The Python computer language has a low barrier to entry, so it is good to start with it. Writing code is all about problem solving. Don’t learn to code; code to learn. Consider the social implications of what your bot does. Aim for familiarity, not fluency, at first, and make it easy to begin running code, then make something fun that users can show off to other people. Provide a glossary and write your own plain-language definitions.

Takeaways:

• Bot literacy helps us to better understand our networked world.
• Making bots is an effective entry point into programming.
• A scaffolded approach is a god way to teach programming.
• Include creative time.

The workshop was successful; participants provided favorable feedback.