How to get started with Git for biomedical researchers

Learning basic Git is a pretty essential skill for biomedical researchers sharing code—and increasingly, data—with others.

Thankfully, there’s a wealth of helpful guides out there on how to get started. Here are 4 resources we recommend:

  1. Why use Git? Skim “Git can facilitate greater reproducibility and increased transparency in science” by Karthik Ram
  2. Are you a visual learner? Watch Data School’s Git and GitHub videos for beginners
  3. Want to learn by doing, and already have Git on your command line? Begin with Software Carpentry’s self-guided lesson on how to get started with Git
  4. Want to work with others? You’ll probably want to set up a free account on Github (the most commonly used Git hosting/collaboration site) — and if you want to upgrade, you can take advantage of their education discounts

 

Balancing the benefits and risks in using Cloud Technology for Research

Contributed by Jamie Lam, Data Security Compliance Manager, UCSF School of Medicine

Cloud technology offers many benefits to researchers, such as:

  • ease of use,
  • rapid deployment, and
  • reduced costs.

At the same time, there are also some hidden implications to using a cloud service provider, including security obligations that may not be well understood.

While a well-designed cloud computing system can be safer than traditional client-server systems, when you are considering a cloud service, you must understand the benefits and risks, as well as your responsibilities in keeping sensitive data secure.

A couple of important points:

1) UC has standard contracts used with providers that protect our institutions’ security and assets. You should always work with procurement so they can ensure that the appropriate agreement is in place.

2) If disruption to services will negatively impact your research or operations, you should negotiate a Service Level Agreement (SLA) based on your needs.

However, don’t just rely on the signed contracts – you should always vet the vendors to confirm that they really are protecting our patients and our reputation.

UCSF has many resources to help you select the right vendor and ensure that your application and sensitive data are secure:

Interested in understanding more about Cloud Services and their benefits and risks? Take a look at this presentation by the School of Medicine Data Security Compliance Program: Securing Data in the Cloud

The 7 Keys to Maximizing Email Survey Response Rates

Lessons learned after achieving a high email survey response rate for a recent NSF Grant Study on UCSF Profiles.  Brought to you by Anirvan Chatterjee & Nooshin Latour

Your recipients don’t care about your email

The average office worker may get over 100 emails per day. Swiftly deleting or ignoring unwanted email can be the only way to stay afloat. These seven best practices will help ensure your email gets opened, read, and acted on — and not ignored or deleted.

We believe that our email marketing tactics and using customized data to drive up survey responses is widely applicable across research studies that can utilize targeted user data to increase study participation. Continue reading

UCSF Profiles Team Invited to Geneva, Switzerland

The UCSF Profiles Team got more international attention for its enhancements to the Profiles product and the level of engaged users last year. Over the past several months, the Special Program for Research and Training in Tropical Diseases (TDR) has been in talks with UCSF Profiles to gain insight and plan an approach to create a system that will show and track their researchers’ work around the globe. TDR is a global collaborative program sponsored by the United Nations Children’s Fund (UNICEF), the United Nations Development Program (UNDP), the World Bank and World Health Organization (WHO). Continue reading

We’ve completed our NSF Grant! UCSF Profiles and its use by external partners

UCSF Profiles is an example of a Research networking system (RNS). These systems provide automated aggregation and mining of information to create profiles and networks of the people that make up an academic institution. RNS’s have in effect, become a new kind of ‘front door’ for the university, providing access to the university’s intellectual capital in a manner previously unattainable — i.e. one focused on expertise rather than schools or departments, thus intermingling experts regardless of where they’re officially housed. Against this backdrop, we wanted to understand how such a tool might enhance access to academic expertise by external partners, specifically industry, and improve UCSF’s response to industry interest. Continue reading

UCSF collaborations, visualized

UCSF researchers often work closely with one another, across departments. We used data from UCSF Profiles to visualize how different departments work together, based on co-authorship patterns.

Visualization details: Data is drawn from UCSF Profiles, and includes all publications co-authored by current UCSF researchers from two more departments and listed on PubMed. The size of each department corresponds with the number of publications that members have published that include partnerships with other departments. The width of the lines connecting departments corresponds to the number of publications between two departments. Colors indicate clusters of departments that often publish collaboratively, based on network modularity. No scaling is done to account for varying sizes of different departments.

Click to view full-size image

UCSF internal collaborations, by department, based on publication co-authorship

Study: The Science Behind Twitter ‘Tribes’

The article ‘You Are What You Tweet’ from MediaBistro alluded to a recent study by EPJ Science finding that ‘word usage mirrors community structure in the online social network Twitter.’

It made me think about how we communicate with people interested in accelerating biomedical research – do we employ a unique language pattern? On the flip side: How should our language change when trying to reach a wider audience (outside our social network), engage new communities and partners?

Image

EPJ Abstract
Background:
Language has functions that transcend the transmission of information and varies with social context. To find out how language and social network structure interlink, we studied communication on Twitter, a broadly-used online messaging service.

Results: We show that the network emerging from user communication can be structured into a hierarchy of communities, and that the frequencies of words used within those communities closely replicate this pattern. Consequently, communities can be characterised by their most significantly used words. The words used by an individual user, in turn, can be used to predict the community of which that user is a member.

Conclusions: This indicates a relationship between human language and social networks, and suggests that the study of online communication offers vast potential for understanding the fabric of human society. Our approach can be used for enriching community detection with word analysis, which provides the ability to automate the classification of communities in social networks and identify emerging social groups.