Science Boot Camp West

Last week Stanford Libraries hosted the third annual Science Boot Camp West (SBCW 2015),

“… building on the great Science Boot Camp events held at the University of Colorado, Boulder in 2013 and at the University of Washington, Seattle in 2014. Started in Massachusetts and spreading throughout the USA, science boot camps for librarians are 2.5 day events featuring workshops and educational presentations delivered by scientists with time for discussion and information sharing among all the participants. Most of the attendees are librarians involved in supporting research in the sciences, engineering, medicine or technology although anybody with an interest in science research is welcome.”

As a former researcher and newcomer to the library and research data management (RDM) scenes, I was already familiar with many of the considerable challenges on both sides of the equation (Jake Carlson recently summarized the plight of data librarians). What made SBCW 2015 such an excellent event is that it brought researchers and librarians together to identify immediate opportunities for collaboration. It also showcased examples of Stanford libraries and librarians directly facilitating the research process, from the full-service Stanford Geospatial Center to organizing Software and Data Carpentry workshops (more on this below, and from an earlier post).

Collaboration: Not just a fancy buzzword

The mostly Stanford-based researchers were generous with their time, introducing us to high-level concerns (e.g., why electrons do what they do in condensed matter) as well as more practical matters (e.g., shopping for alternatives to Evernote—yikes—for electronic lab notebooks [ELNs]). They revealed the intimate details of their workflows and data practices (Dr. Audrey Ellerbee admitted that it felt like letting guests into her home to find dirty laundry strewn everywhere, a common anxiety among researchers that in her case was unwarranted), flagged the roadblocks, and presented a constant stream of ideas for building relationships across disciplines and between librarians and researchers.

From the myriad opportunities for library involvement, here are some of the highlights:

  • Facilitate community discussions of best practices, especially for RDM issues such as programming, digital archiving, and data sharing
  • Consult with researchers about available software solutions (e.g., ELNs such as Labguru and LabArchives; note: representatives from both of these companies gave presentations and demonstrations at SBCW 2015), connect them with other users on campus, and provide help with licensing
  • Provide local/basic IT support for students and researchers using commercial products such as ELNs (e.g., maintain FAQ lists to field common questions)
  • Leverage experience with searching databases to improve delivery of informatics content to researchers (e.g., chemical safety data)
  • Provide training in and access to GIS and other data visualization tools

A winning model

The final half-day was dedicated to computer science-y issues. Following a trio of presentations involving computational workflows and accompanying challenges (the most common: members of the same research group writing the same pieces of code over and over with scant documentation and zero version control), Tracy Teal (Executive Director of Data Carpentry) and Amy Hodge (Science Data Librarian at Stanford) introduced a winning model for improving everyone’s research lives.

Software Carpentry and Data Carpentry are extremely affordable 2-day workshops that present basic concepts and tools for more effective programming and data handling, respectively. Training materials are openly licensed (CC-BY) and workshops are led by practitioners for practitioners allowing them to be tailored to specific domains (genomics, geosciences, etc.). At present the demand for these (international) workshops exceeds the capacity to meet it … except at Stanford. With local, library-based coordination, Amy has brokered (and in some cases taught) five workshops for individual departments or research groups (who covered the costs themselves). This is the very thing I wished for as a graduate student—muddling through databases and programming in R on my own—and I think it should be replicated at every research institution. Better yet, workshops aren’t restricted to the sciences; Data Carpentry is developing training materials for techniques used in the digital humanities such as text mining.

Learning to live outside of the academic bubble

Another, subtler theme that ran throughout the program was the need/desire to strengthen connections between the academy and industry. Efforts along these lines stand to improve the science underlying matters of public policy (e.g., water management in California) and public health (e.g., new drug development). They also address the mounting pressure placed on researchers to turn knowledge into products. Mark Smith addressed this topic directly during his presentation on ChEM-H: a new Stanford initiative for supporting research across Chemistry, Engineering, and Medicine to understand and advance Human Health. I appreciated that Mark—a medicinal chemist with extensive experience in both sectors—and others emphasized the responsibility to prepare students for jobs in a rapidly shifting landscape with increasing demand for technical skills.

Over the course of SBCW 2015 I met engaged librarians, data managers, researchers, and product managers, including some repeat attendees who raved about the previous two SBCW events; the consensus seemed to be that the third was another smashing success. Helen Josephine (Head of the Engineering Library at Stanford who chaired the organizing committee) is already busy gathering feedback for next year.

SBCW 2015 at Stanford included researchers from:

Gladstone Institutes in San Francisco

ChEM-H Stanford’s lab for Chemistry, Engineering & Medicine for Human Health

Water in the West Institute at Stanford

NSF Engineering Research Center for Re-inventing the Nation’s Urban Water Infrastructure (ReNUWIt)

DeepDive

Special project topics on Software and Data Carpentry with Physics and BioPhysics faculty and Tracy Teal from Software Carpentry.

Many thanks to:

Helen Josephine, Suzanne Rose Bennett, and the rest of the Local Organizing Committee at Stanford. Sponsored by the National Network of Libraries of Medicine – Pacific Southwest Region, Greater Western Library Alliance, Stanford University Libraries, SPIE, IEEE, Springer Science+Business Media, Annual Reviews, Elsevier.

From Flickr by Paula Fisher (It was just like this, but indoors, with coffee, and powerpoints.)

From Flickr by Paula Fisher (It was just like this, but indoors, with coffee, and powerpoints.)

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Data metrics survey results published

Today, we are pleased to announce the publication Making Data Count in Scientific Data. John Kratz and Carly Strasser led the research effort to understand the needs and values of both the researchers who create and use data and of the data managers who preserve and publish it. The Making Data Count project is a collaboration between the CDL, PLOS, and DataONE to define and implement a practical suite of metrics for evaluating the impact of datasets, which is a necessary prerequisite to widespread recognition of datasets as first class scholarly objects.

We started the project with research to understand what metrics would be meaningful to stakeholders and what metrics we can practically collect. We conducted a literature review, focus groups, and– the subject of today’s paper–  a pair of online surveys for researchers and data managers.

In November and December of 2014, 247 researchers and 73 data repository managers answered our questions about data sharing, use, and metrics.Graph of interest in various metrics Survey and anonymized data are available in the Dash repository. These responses told us, among other things, which existing Article Level Metrics (ALMs) might be profitably applied to data:

  • Social media: We should not worry excessively about capturing social media (Twitter, Facebook, etc.) activity around data yet, because there is not much to capture. Only 9% of researchers said they would “definitely” use social media to look for a dataset.
  • Page views: Page views are widely collected by repositories but neither researchers nor data managers consider them meaningful. (It stands to reason that, unlike a paper, you can’t have engaged very deeply with a dataset if all you’ve done is read about it.)
  • Downloads: Download counts, on the other hand, are both highly valuable and practical to collect. Downloads were a resounding second-choice metric for researchers and 85% of repositories already track them.
  • Citations: Citations are the coin of the academic realm. They were by far the most interesting metric to both researchers and data managers. Unfortunately, citations are much more difficult than download counts to work with, and relatively few repositories track them. Beyond technical complexity, the biggest challenge is cultural: data citation practices are inconsistent at best, and formal data citation is rare. Despite the difficulty, the value of citations is too high to ignore, even in the short term.

We have already begun to collect data on the sample project corpus– the entire DataONE collection of 100k+ datasets. Using this pilot corpus, we see preliminary indications of researcher engagement with data across a number of online channels not previously thought to be in use by scholars. The results of this pilot will complement the survey described in today’s paper with real measurement of data-related activities “in the wild.”

For more conclusions and in-depth discussion of the initial research, see the paper, which is open access and available here: http://dx.doi.org/10.1038/sdata.2015.39. Stay tuned for analysis and results of the DataONE data-level metrics data on the Making Data Count project page: http://lagotto.io/MDC/.

Does Your Library Delight You?

In a recent opinion piece in Forbes, Steve Denning provocatively asks, “Do we need libraries?

As a digital librarian, my short answer is “Yes, of course we need libraries!” But, Denning makes many excellent points in cautioning that the same disruptive threats faced by many industries — think taxis and Uber, or hotels and AirBnB, for example — are also a threat to libraries. Denning argues that in today’s world, libraries must change their management practices and offerings in order to remain relevant. The computer age is not just about computerizing, he explains, but also about a fundamental shift that puts the customer or the user in control:

600px-Smiley.svg

[From wikimedia user: Pumbaa80]

“… the most important thing that computers and the internet have done is not just to make things faster and easier for organizations. Even more importantly, they have shifted the balance of power in the marketplace from the seller to the buyer. The customer is now in charge. The customer has choices and good information about those choices. Unless customers and users are delighted, they can and will take their business elsewhere.”

To be clear, I would never suggest “Uber-izing” libraries, but there is much that those of us in the library world can learn from these evolving user-centered models.

Denning suggests a handful of “right” and “wrong” approaches to the future of libraries. Among the right approaches is the importance of focusing on how to “delight the user or customer.” We need to create services that truly meet or exceed the expectations of library users. We need to restructure ourselves in a way that ignites continuous innovation. And, we need to think about how to create services for users that they haven’t even thought of yet, while also continuing to perform the services that our users really love about our libraries, only faster and better.

Shifting the focus of academic research libraries to new models and areas of focus is not an easy task. But that’s exactly what’s happening at the UC Berkeley Libraries with the launch of the UC Berkeley’s Research Data Management (RDM) program, a joint venture between UCB Libraries and Berkeley’s Research Information Technologies (RIT) group. I recently attended the first public workshop for this program, and I’d say this initiative is a continuous affirmation that there’s a clear and compelling role for libraries in the future.

The UCB Libraries continue to re-tool themselves to meet the exponentially growing need to provide solutions for managing, preserving, and providing access to research data. They are proving innovative in their partnership with Research Information Technologies (RIT).Together, the Libraries and RIT bring to the table an excellent complement of staff and skills that, through collaboration, will help tackle the complex challenges of data management. From the get go, the Research Data Management program has focused on being inclusive. At the first workshop, for instance, they cast a wide net to ensure attendance from a variety of disciplines and departments. They also sought everyone’s input and challenged us to think creatively about new solutions. And finally, they are focusing their efforts on connecting things that are working well in the library and across the campus with external resources that users can tap into.

The Research Data Management program’s three goals for the coming year include:

  • Training and Workshop Series: An in-person space to learn and share ideas across the campus, including hands-on training as well as tackling big picture topics such as policy, best practices and governance issues.
  • Rich, Online Resource Guide: A one-stop shopping for researchers to find resources to support their work all along the research cycle.
  • Consultative Services: A personalized service to support research needs.

With the implementation of new funder requirements and the increased pressure to share data as well as the fragility of digital media, researchers are feeling the pressure to come up with sustainable solutions for data management. Through the RDM program, the UC Berkeley Libraries’ are taking steps towards providing news services that users need, and others that they may not even know that they need.

At this first workshop, there was great energy and excitement in the room. I was certainly delighted and I think UCB faculty, students, and staff will also be.

Meet the Team

The group spearheading program includes:

  • Norm Cheng, Senior Project Manager
  • Harrison Dekker, Coordinator Data Services
  • Susan Edwards, Head, Social Sciences Division
  • Mary Elings, Archivist for Digital Collections
  • David Greenbaum, Director, Research Information Technologies (RIT)
  • Chris Hoffman, Manager, Informatics Services
  • Rick Jaffe, Web Developer
  • John Lowe, Technical Lead and Manager for the CollectionSpace service
  • Erik Mitchell, Associate University Librarian, Director of Digital Initiatives and Collaborative Services
  • Felicia Poe, Interim UC Curation Center Director, California, Digital Library

Make Data Rain

Last October, UC3,  PLOS, and DataONE launched Making Data Count, a collaboration to develop data-level metrics (DLMs). This 12-month National Science Foundation-funded project will pilot a suite of metrics to track and measure data use that can be shared with funders, tenure & promotion committees, and other stakeholders.

Featured image

[image from Freepik]

To understand how DLMs might work best for researchers, we conducted an online survey and held a number of focus groups, which culminated on a very (very) rainy night last December in a discussion at the PLOS offices with researchers in town for the 2014 American Geophysical Union Fall Meeting.

Six eminent researchers participated:

Much of the conversation concerned how to motivate researchers to share data. Sources of external pressure that came up included publishers, funders, and peers. Publishers can require (as PLOS does) that, at a minimum, the data underlying every figure be available. Funders might refuse to ‘count’ publications based on unavailable data, and refuse to renew funding for projects that don’t release data promptly. Finally, other researchers– in some communities, at least– are already disinclined to work with colleagues who won’t share data.

However, Making Data Count is particularly concerned with the inverse– not punishing researchers who don’t share, but rewarding those who do. For a researcher, metrics demonstrating data use serve not only to prove to others that their data is valuable, but also to affirm for themselves that taking the time to share their data is worthwhile. The researchers present regarded altmetrics with suspicion and overwhelmingly affirmed that citations are the preferred currency of scholarly prestige.

Many of the technical difficulties with data citation (e.g., citing  dynamic data or a particular subset) came up in the course of the conversation. One interesting point was raised by many: when citing a data subset, the needs of reproducibility and credit diverge. For reproducibility, you need to know exactly what data has been used– at a maximum level of granularity. But, credit is about resolving to a single product that the researcher gets credit for, regardless of how much of the dataset or what version of it was used– so less granular is better.

We would like to thank everyone who attended any of the focus groups. If you have ideas about how to measure data use, please let us know in the comments!

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We are Hiring a DMPTool Manager!

Do you love all things data management as much as we do? Then join our team! We are hiring a person to help manage the DMPTool, including development prioritization, promotion, outreach, and education. The position is funded for two years with the potential for an extension pending funding and budgets. You would be based in the amazing city of Oakland CA, home of the California Digital Library. Read more at jobs.ucop.edu or download the PDF description: Data Management Product Manager (4116).

Job Duties

Product Management (30%): Ensure the DMPTool remains a viable and relevant application. Update funder requirements, maintain the integrity of publicly available DMPs, contact partner institutions to report issues, and review DMPTool guidance and content for currency. Evaluates and presents new technologies and industry trends. Recommends those that are applicable to current products or services and the organization’s long-range, strategic plans. Identifies, organizes, and participates in technical discussions with key advisory groups and other customers/clients. Identifies additional opportunities for value added product/service delivery based on customer/client interaction and feedback.

Marketing and Ourtreach (20%): Develop and implement strategies for promoting the DMPTool. Create marketing materials, update website content, contacting institutions, and present at workshops and/or conferences. Develops and participates in marketing and professional outreach activities and informational campaigns to raise awareness of product or service including communicating developments and updates to the community via social media. This includes maintaining the DMPTool blog, Twitter and Facebook accounts, GitHub Issues, and listservs.

Project Management (30%): Develops project plans including goals, deliverables, resources, budget and timelines for enhancements of the DMPTool. Acting as product/service liaison across the organization, external agencies and customers to ensure effective production, delivery and operation of the DMPTool.

Strategic Planning (10%): Assist in strategic planning, prioritizing and guiding future development of the DMPTool. Pursue outside collaborations and funding opportunities for future DMPTool development including developing an engaged community of DMPTool users (researchers) and software developers to contribute to the codebase. Foster and engage open source community for future maintenance and enhancement.

Reporting (10%): Provides periodic content progress reports outlining key activities and progress toward achieving overall goals. Develops and reports on metrics/key performance indicators and provides corresponding analysis.

To apply, visit jobs.ucop.edu (Requisition No. 20140735)

From Flickr by Brenda Gottsabend

From Flickr by Brenda Gottsabend

Announcing The Dash Tool: Data Sharing Made Easy

We are pleased to announce the launch of Dash – a new self-service tool from the UC Curation Center (UC3) and partners that allows researchers to describe, upload, and share their research data. Dash helps researchers perform the following tasks:

  • Prepare data for curation by reviewing best practice guidance for the creation or acquisition of digital research data.
  • Select data for curation through local file browse or drag-and-drop operation.
  • Describe data in terms of the DataCite metadata schema.
  • Identify data with a persistent digital object identifier (DOI) for permanent citation and discovery.
  • Preserve, manage, and share data by uploading to a public Merritt repository collection.
  • Discover and retrieve data through faceted search and browse.

Who can use Dash?

There are multiple instances of the Dash tool that all have similar functions, look, and feel.  We took this approach because our UC campus partners were interested in their Dash tool having local branding (read more). It also allows us to create new Dash instances for projects or partnerships outside of the UC (e.g., DataONE Dash and our Site Descriptors project).

Researchers at UC Merced, UCLA, UC Irvine, UC Berkeley, or UCOP can use their campus-specific Dash instance:

Other researchers can use DataONE Dash (oneshare.cdlib.org). This instance is available to anyone, free of charge. Use your Google credentials to deposit data.

Note: Data deposited into any Dash instance is visible throughout all of Dash. For example, if you are a UC Merced researcher and use dash.ucmerced.edu to deposit data, your dataset will appear in search results for individuals looking for data via any of the Dash instances, regardless of campus affiliation.

See the Users Guide to get started using Dash.

Stay connected to the Dash project:

Dash Origins

The Dash project began as DataShare, a collaboration among UC3, the University of California San Francisco Library and Center for Knowledge Management, and the UCSF Clinical and Translational Science Institute (CTSI). CTSI is part of the Clinical and Translational Science Award program funded by the National Center for Advancing Translational Sciences at the National Institutes of Health (Grant Number UL1 TR000004).

Fontana del Nettuno

Sound the horns! Dash is live! “Fontana del Nettuno” by Sorin P. from Flickr.

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Data: Do You Care? The DLM Survey

We all know that data is important for research. So how can we quantify that? How can you get credit for the data you produce? What do you want to know about how your data is used?

If you are a researcher or data manager, we want to hear from you. Take this 5-10 minute survey and help us craft data-level metrics:

surveymonkey.com/s/makedatacount

Please share widely! The survey will be open until December 1st.

Read more about the project at mdc.plos.org or check out our previous post. Thanks to John Kratz for creating the survey and jumping through IRB hoops!

What do you think of data metrics? We're listening.  From gizmodo.com. Click for more pics of dogs + radios.

What do you think of data metrics? We’re listening.
From gizmodo.com. Click for more pics of dogs + radios.

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Dash Project Receives Funding!

We are happy to announce the Alfred P. Sloan Foundation has funded our project to improve the user interface and functionality of our Dash tool! You can read the full grant text at http://escholarship.org/uc/item/2mw6v93b.

More about Dash

Dash is a University of California project to create a platform that allows researchers to easily describe, deposit and share their research data publicly. Currently the Dash platform is connected to the UC3 Merritt Digital Repository; however, we have plans to make the platform compatible with other repositories using protocols during our Sloan-funded work. The Dash project is open-source; read more on our GitHub site. We encourage community discussion and contribution via GitHub Issues.

Currently there are five instances of the Dash tool available:

We plan to launch the new DataONE Dash instance in two weeks; this tool will replace the existing DataUp tool and allow anyone to deposit data into the DataONE infrastructure via the ONEShare repository using their Google credentials. Along with the release of DataONE Dash, we will release Dash 1.1 for the live sites listed above. There will be improvements to the user interface and experience.

The Newly Funded Sloan Project

Problem Statement

Researchers are not archiving and sharing their data in sustainable ways. Often data sharing involves using commercially owned solutions, posting data on personal websites, or submitting data alongside articles as supplemental material. A better option for data archiving is community repositories, which are owned and operated by trusted organizations (i.e., institutional or disciplinary repositories). Although disciplinary repositories are often known and used by researchers in the relevant field, institutional repositories are less well known as a place to archive and share data.

Why aren’t researchers using institutional repositories?

First, the repositories are often not set up for self-service operation by individual researchers who wish to deposit a single dataset without assistance. Second, many (or perhaps most) institutional repositories were created with publications in mind, rather than datasets, which may in part account for their less-than-ideal functionality. Third, user interfaces for the repositories are often poorly designed and do not take into account the user’s experience (or inexperience) and expectations. Because more of our activities are conducted on the Internet, we are exposed to many high-quality, commercial-grade user interfaces in the course of a workday. Correspondingly, researchers have expectations for clean, simple interfaces that can be learned quickly, with minimal need for contacting repository administrators.

Our Solution

We propose to address the three issues above with Dash, a well-designed, user friendly data curation platform that can be layered on top of existing community repositories. Rather than creating a new repository or rebuilding community repositories from the ground up, Dash will provide a way for organizations to allow self-service deposit of datasets via a simple, intuitive interface that is designed with individual researchers in mind. Researchers will be able to document, preserve, and publicly share their own data with minimal support required from repository staff, as well as be able to find, retrieve, and reuse data made available by others.

Three Phases of Work

  1. Requirements gathering: Before the design process begins, we will build requirements for researchers via interviews and surveys
  2. Design work: Based on surveys and interviews with researchers (Phase 1), we will develop requirements for a researcher-focused user interface that is visually appealing and easy to use.
  3. Technical work: Dash will be an added-value data sharing platform that integrates with any repository that supports community protocols (e.g., SWORD (Simple Web-service Offering Repository Deposit).

The dash is a critical component of any good ascii art. By reddit user Haleljacob

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New Project: Citing Physical Spaces

A few months ago, the UC3 group was contacted by some individuals interested in solving a problem: how should we reference field stations? Rob Plowes from University of Texas/Brackenridge Field Lab emailed us:

I am on a [National Academy of Sciences] panel reviewing aspects of field stations, and we have been discussing a need for data archiving. One idea proposed is for each field station to generate a simple document with a DOI reference to enable use in publications that make reference to the field station. Having this DOI document would enable a standardized citation that could be tracked by an online data aggregator.

We thought this was a great idea and started having a few conversations with other groups (LTER, NEON, etc.) about its feasibility. Fast forward to two weeks ago, when Plowes and Becca Fenwick of UC Merced presented our more fleshed out idea to the OBFS/NAML Joint Meeting in Woods Hole, MA. (OBFS: Organization of Biological Field Stations, and NAML: National Association of Marine Laboratories). The response was overwhelmingly positive, so we are proceeding with the idea in earnest here at the CDL.

The intent of this blog post is to gather feedback from the broader community about our idea, including our proposed metadata fields, our plans for implementation, and whether there are existing initiatives or groups that we should be aware of and/or partner with moving forward.

In a Nutshell

Problem: Tracking publications associated with a field station or site is difficult. There is no clear or standard way to cite field station descriptions.

Proposal: Create individual, citable “publications” with associated persistent identifiers for each field station (more generically called a “site”). Collect these Site Descriptors in the general use DataONE repository, ONEShare. The user interface will be a new instance of the existing UC3 Dash service (under development) with some modifications for Site Descriptors.

What we need from you: 

Moving forward: We plan on gathering community feedback for the next few months, with an eye towards completing a pilot version of the interface by February 2015. We will be ramping up Dash development over the next 12 months thanks to recent funding from the Alfred P. Sloan Foundation, and this development work will include creating a more robust version of the Site Descriptors database.

Project Partners:

  • Rob Plowes, UT Austin/Brackenridge Field Lab
  • Mark Stromberg, UC Berkeley/UC Natural Reserve System
  • Kevin Browne, UC Natural Reserve System Information Manager
  • Becca Fenwick, UC Merced
  • UC3 group
  • DataONE organization

Lovers Point Laboratory (1930), which was later renamed Hopkins Marine Laboratory. From Calisphere, contributed by Monterey County Free Libraries.

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The 10 Things Every New Grad Student Should Do

It’s now mid-October, and I’m guessing that first year graduate students are knee-deep in courses, barely considering their potential thesis project. But for those that can multi-task, I have compiled this list of 10 things that you should undertake in your first year as a grad student. These aren’t just any 10 things… they are 10 steps you can take to make sure you contribute to a culture shift towards open science. Some a big steps, and others are small, but they will all get you (and the rest of your field) one step closer to reproducible, transparent research.

1. Learn to code in some language. Any language.

Here’s the deal: it’s easier to use black-box applications to run your analyses than to create scripts. Everyone knows this. You put in some numbers and out pop your results; you’re ready to write up your paper and get that H-index headed upwards. But this approach will not cut the mustard for much longer in the research world. Researchers need to know about how to code. Growing amounts and diversity of data, more interdisciplinary collaborators, and increasing complexity of analyses mean that no longer can black-box models, software, and applications be used in research. The truth is, if you want your research to be reproducible and transparent, you must code. In a 2013 article “The Big Data Brain Drain: Why Science is in Trouble“, Jake Vanderplas argues that

In short, the new breed of scientist must be a broadly-trained expert in statistics, in computing, in algorithm-building, in software design, and (perhaps as an afterthought) in domain knowledge as well.

I learned MATLAB in graduate school, and experimented with R during a postdoc. I wish I’d delved into this world earlier, and had more skills and knowledge about best practices for scientific software. Basically, I wish I had attended a Software Carpentry bootcamp.

The growing number of Software Carpentry (SWC) bootcamps are more evidence that researchers are increasingly aware of the importance of coding and reproducibility. These bootcamps teach researchers the basics of coding, version control, and similar topics, with the potential for customizing the course’s content to the primary discipline of the audience. I’m a big fan of SWC – read more in my blog post on the organization. Check out SWC founder Greg Wilson’s article on some insights from his years in teaching bootcamps: Software Carpentry: Lessons Learned.

2. Stop using Excel. Or at least stop ONLY using Excel.

Most seasoned researchers know that Microsoft Excel can be potentially problematic for data management: there are loads of ways to manipulate, edit, reorder, and change your data without really knowing exactly what you did. In nerd terms, the trail of dataset changes is known as provenance; generally Excel is terrible at documenting provenance. I wrote about this a few years ago on the blog, and we mentioned a few of the more egregious ways people abuse Excel in our F1000Research publication on the DataUp tool. More recently guest blogger Kara Woo wrote a great post about struggles with dates in Excel.

Of course, everyone uses Excel. In our surveys for the DataUp project, about 88% of the researchers we interviewed used Excel at some point in their research. And we can’t expect folks to stop using it: it’s a great tool! It should, however, be used carefully. For instance, don’t manipulate the sole copy of your raw data in Excel; keep your raw data raw. Use Excel to explore your data, but use other tools to clean and analyze it, such as R, Python, or MATLAB (see #1 above on learning to code). For more help with spreadsheets, see our list of resources and tools: UC3 Spreadsheet Help.

3. Learn about how to properly care for your data.

You might know more about your data than anyone else, but you aren’t so smart when it comes stewardship your data. There are some great guidelines for how best to document, manage, and generally care for your data; I’ve collected some of my favorites here on CiteULike with the tag best_practices. Pick one (or all of them) to read and make sure your data don’t get short shrift.

4. Write a data management plan.

I know, it sounds like the ultimate boring activity for a Friday night. But these three words (data management plan) can make a HUGE difference in the time and energy spent dealing with data during your thesis. Basically, if you spend some time thinking about file organization, sample naming schemes, backup plans, and quality control measures, you can save many hours of heartache later. Creating a data management plan also forces you to better understand best practices related to data (#3 above). Don’t know how to start? Head over to the DMPTool to write a data management plan. It’s free to use, and you can get an idea for the types of things you should consider when embarking on a new project. Most funders require data management plans alongside proposal submissions, so you might as well get the experience now.

5. Read Reinventing Discovery by Michael Nielsen.

 Reinventing Discovery: The New Era of Networked Science by Michael Nielsen was published in 2013, and I’ve since heard it referred to as the Bible for Open Science, and the must-read book for anyone interested in engaging in the new era of 4th paradigm research. I’ve only just recently read the book, and wow. I was fist-bumping quite a bit while reading it, which must have made fellow airline passengers wonder what the fuss was about. If they had asked, I would have told them about Nielsen’s stellar explanation of the necessity for and value of openness and transparency in research, the problems with current incentive structures in science, and the steps we should all take towards shifting the culture of research to enable more connectivity and faster progress. Just writing this blog post makes me want to re-read the book.

6. Learn version control.

My blog post, Git/GitHub: a Primer for Researchers covers much of the importance of version control. Here’s an excerpt:

From git-scm.com, “Version control is a system that records changes to a file or set of files over time so that you can recall specific versions later.”  We all deal with version control issues. I would guess that anyone reading this has at least one file on their computer with “v2” in the title. Collaborating on a manuscript is a special kind of version control hell, especially if those writing are in disagreement about systems to use (e.g., LaTeX versus Microsoft Word). And figuring out the differences between two versions of an Excel spreadsheet? Good luck to you. TheWikipedia entry on version control makes a statement that brings versioning into focus:

The need for a logical way to organize and control revisions has existed for almost as long as writing has existed, but revision control became much more important, and complicated, when the era of computing began.

Ah, yes. The era of collaborative research, using scripting languages, and big data does make this issue a bit more important and complicated. Version control systems can make this much easier, but they are not necessarily intuitive for the fledgling coder. It might take a little time (plus attending a Software Carpentry Bootcamp) to understand version control, but it will be well worth your time. As an added bonus, your work can be more reproducible and transparent by using version control. Read Karthik Ram’s great article, Git can facilitate greater reproducibility and increased transparency in science.

7. Pick a way to communicate your science to the public. Then do it.

You don’t have to have a black belt in Twitter or run a weekly stellar blog to communicate your work. But you should communicate somehow. I have plenty of researcher friends who feel exasperated by the idea that they need to talk to the public about their work. But the truth is, in the US this communication is critical to our research future. My local NPR station recently ran a great piece called Why Scientists are seen as untrustworthy and why it matters. It points out that many (most?) scientists aren’t keen to spend a lot of time engaging with the broader public about their work. However:

…This head-in-the-sand approach would be a big mistake for lots of reasons. One is that public mistrust may eventually translate into less funding and so less science. But the biggest reason is that a mistrust of scientists and science will have profound effects on our future.

Basically, we are avoiding the public at our own peril. Science funding is on the decline, we are facing increasing scrutiny, and it wouldn’t be hyperbole to say that we are at war without even knowing it. Don’t believe me? Read this recent piece in Science (paywall warning): Battle between NSF and House science committee escalates: How did it get this bad?

So start talking. Participate in public lecture series, write a guest blog post, talk about your research to a crotchety relative at Thanksgiving, or write your congressman about the governmental attack on science.

8. Let everyone watch.

Consider going open. That is, do all of your science out in the public eye, so that others can see what you’re up to. One way to do this is by keeping an open notebook. This concept throws out the idea that you should be a hoarder, not telling others of your results until the Big Reveal in the form of a publication. Instead, you keep your lab notebook (you do have one, right?) out in a public place, for anyone to peruse. Most often an open notebook takes the form of a blog or a wiki, and the researcher updates their notebook daily, weekly, or whatever is most appropriate. There are links to data, code, relevant publications, or other content that helps readers, and the researcher themselves, understand the research workflow. Read more in these two blog posts: Open Up  and Open Science: What the Fuss is About.

9. Get your ORCID.

ORCID stands for “Open Researcher & Contributor ID”. The ORCID Organization is an open, non-profit group working to provide a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. The endgame is to support the creation of a permanent, clear and unambiguous record of scholarly communication by enabling reliable attribution of authors and contributors. Basically, researcher identifiers are like social security numbers for scientists. They unambiguously identify you throughout your research life.

Lots of funders, tools, publishers, and universities are buying into the ORCID system. It’s going to make identifying researchers and their outputs much easier. If you have a generic, complicated, compound, or foreign name, you will especially benefit from claiming your ORCID and “stamping” your work with it. It allows you to claim what you’ve done and keep you from getting mixed up with that weird biochemist who does studies on the effects of bubble gum on pet hamsters. Still not convinced? I wrote a blog post a while back that might help.

10. Publish in OA journals, or make your work OA afterward.

A wonderful post by Michael White, Why I don’t care about open access to research: and why you should, captures this issue well:

It’s hard for me to see why I should care about open access…. My university library can pay for access to all of the scientific journals I could wish for, but that’s not true of many corporate R&D departments, municipal governments, and colleges and schools that are less well-endowed than mine. Scientific knowledge is not just for academic scientists at big research universities.

It’s easy to forget that you are (likely) among the privileged academics. Not all researchers have access to publications, and this is even more true for the general public. Why are we locking our work in the Ivory Tower, allowing for-profit publishers to determine who gets to read our hard-won findings? The Open Access movement is going full throttle these days, as evidenced by increasing media coverage (see “Steal this research paper: you already paid for it” from MotherJones, or The Guardian’s blog post “University research: if you believe in openness, stand up for it“). So what can you do?

Consider publishing only in open access journals (see the Directory of Open Access Journals). Does this scare you? Are you tied to a disciplinary favorite journal with a high impact factor? Then make your work open access after publishing in a standard journal. Follow my instructions here: Researchers! Make Your Previous Work #OA.

Openness is one of the pillars of a stellar academic career. From Flickr by David Pilbrow.

Openness is the pillar of a good academic career. From Flickr by David Pilbrow.

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