Tag Archives: data management

Understanding researcher needs and values related to software

Software is as important as data when it comes to building upon existing scholarship. However, while there has been a small amount of research into how researchers find, adopt, and credit it, there is a comparative lack of empirical data on how researchers use, share, and value their software.

The UC Berkeley Library and the California Digital Library are investigating researchers’ perceptions, values, and behaviors in regards to software generated as part of the research process. If you are a researcher, it would be greatly appreciated if you could spare 10-15 minutes to complete the following survey:

Take the survey now!

The results of this survey will help us better understand researcher needs and values related to software and may also inform the development of library services related to software best practices, code sharing, and the reproducibility of scholarly activity.

If you have questions about our study or any problems accessing the survey, please contact yasminal@berkeley.edu or John.Borghi@ucop.edu.

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Dispatches from PIDapalooza

Last month, California Digital Library, ORCID, Crossref, and Datacite brought together the brightest minds in scholarly infrastructure to do the impossible: make a conference on persistent identifiers fun!

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Usually discussions about persistent identifiers (PIDs) and networked research are dry and hard to get through or we find ourselves discussing the basics and never getting to the meat.

We designed PIDapalooza to attract kindred spirits who are passionate about improving interoperability and the overall quality of our scholarly infrastructure. We knew if we built it, they would come!

The results were fantastic and there was a great showing from the University of California community:

All PIDapalooza presentations are being archived on Figshare: https:/pidapalooza.figshare.com

Take a look and make sure you are following @pidapalooza for word on future PID fun!

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An RDM Model for Researchers: What we’ve learned

Thanks to everyone who gave feedback on our previous blog post describing our data management tool for researchers. We received a great deal of input related to our guide’s use of the term “data sharing” and our guide’s position in relation to other RDM tools as well as quite a few questions about what our guide will include as we develop it further.

As stated in our initial post, we’re building a tool to enable individual researchers to assess the maturity of their data management practices within an institutional or organizational context. To do this, we’ve taken the concept of RDM maturity from in existing tools like the Five Organizational Stages of Digital Preservation, the Scientific Data Management Capability Model, and the Capability Maturity Guide and placed it within a framework familiar to researchers, the research data lifecycle.

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A visualization of our guide as presented in our last blog post. An updated version, including changed made in response to reader feedback, is presented later in this post.

Data Sharing

The most immediate feedback we received was about the term “Data Sharing”. Several commenters pointed out the ambiguity of this term in the context of the research data life cycle. In the last iteration of our guide, we intended “Data Sharing” as a shorthand to describe activities related to the communication of data. Such activities may range from describing data in a traditional scholarly publication to depositing a dataset in a public repository or publishing a data paper. Because existing data sharing policies (e.g. PLOS, The Gates Foundation, and The Moore Foundation) refer specifically to the latter over the former, the term is clearly too imprecise for our guide.

Like “Data Sharing”, “Data Publication” is a popular term for describing activities surrounding the communication of data. Even more than “Sharing”, “Publication” relays our desire to advance practices that treat data as a first class research product. Unfortunately the term is simultaneously too precise and too ambiguous it to be useful in our guide. On one hand, the term “Data Publication” can refer specifically to a peer reviewed document that presents a dataset without offering any analysis or conclusion. While data papers may be a straightforward way of inserting datasets into the existing scholarly communication ecosystem, they represent a single point on the continuum of data management maturity. On the other hand, there is currently no clear consensus between researchers about what it means to “publish” data.

For now, we’ve given that portion of our guide the preliminary label of “Data Output”. As the development process proceeds, this row will include a full range of activities- from description of data in traditional scholarly publications (that may or may not include a data availability statement) to depositing data into public repositories and the publication of data papers.

Other Models and Guides

While we correctly identified that there are are range of rubrics, tools, and capability models with similar aims as our guide, we overstated that ours uniquely allows researchers to assess where they are and where they want to be in regards to data management. Several of the tools we cited in our initial post can be applied by researchers to measure the maturity of data management practices within a project or institutional context.

Below we’ve profiled four such tools and indicated how we believe our guide differs from each. In differentiating our guide, we do not mean to position it strictly as an alternative. Rather, we believe that our guide could be used in concert with these other tools.

Collaborative Assessment of Research Data Infrastructure and Objectives (CARDIO)

CARDIO is a benchmarking tool designed to be used by researchers, service providers, and coordinators for collaborative data management strategy development. Designed to be applied at a variety of levels, from entire institutions down to individual research projects, CARDIO enables its users to collaboratively assess data management requirements, activities, and capacities using an online interface. Users of CARDIO rate their data management infrastructure relative to a series of statements concerning their organization, technology, and resources. After completing CARDIO, users are given a comprehensive set of quantitative capability ratings as well as a series of practical recommendations for improvement.

Unlike CARDIO, our guide does not necessarily assume its users are in contact with data-related service providers at their institution. As we stated in our initial blog post, we intend to guide researchers to specialist knowledge without necessarily turning them into specialists. Therefore, we would consider a researcher making contact with their local data management, research IT, or library service providers for the first time as a positive application of our guide.

Community Capability Model Framework (CCMF)

The Community Capability Model Framework is designed to evaluate a community’s readiness to perform data intensive research. Intended to be used by researchers, institutions, and funders to assess current capabilities, identify areas requiring investment, and develop roadmaps for achieving a target state of readiness, the CCMF encompasses eight “capability factors” including openness, skills and training, research culture, and technical infrastructure. When used alongside the Capability Profile Template, the CCMF provides its users with a scorecard containing multiple quantitative scores related to each capability factor.   

Unlike the CCMF, our guide does not necessarily assume that its users should all be striving towards the same level of data management maturity. We recognize that data management practices may vary significantly between institutions or research areas and that what works for one researcher may not necessarily work for another. Therefore, we would consider researchers understanding the maturity of their data management practices within their local contexts to be a positive application of our guide.

Data Curation Profiles (DCP) and DMVitals

The Data Curation Profile toolkit is intended to address the needs of an individual researcher or research group with regards to the “primary” data used for a particular project. Taking the form of a structured interview between an information professional and a researcher, a DCP can allow an individual research group to consider their long-term data needs, enable an institution to coordinate their data management services, or facilitate research into broader topics in digital curation and preservation.

DMVitals is a tool designed to take information from a source like a Data Curation Profile and use it to systematically assess a researcher’s data management practices in direct comparison to institutional and domain standards. Using the DMVitals, a consultant matches a list of evaluated data management practices with responses from an interview and ranks the researcher’s current practices by their level of data management “sustainability.” The tool then generates customized and actionable recommendations, which a consultant then provides to the researcher as guidance to improve his or her data management practices.  

Unlike DMVitals, our guide does not calculate a quantitative rating to describe the maturity of data management practices. From a measurement perspective, the range of practice maturity may differ between the four stages of our guide (e.g. the “Project Planning” stage could have greater or fewer steps than the “Data Collection” stage), which would significantly complicate the interpretation of any quantitative ratings derived from our guide. We also recognize that data management practices are constantly evolving and likely dependent on disciplinary and institutional context. On the other hand, we also recognize the utility of quantitative ratings for benchmarking. Therefore, if, after assessing the maturity of their data management practices with our guide, a researcher chooses to apply a tool like DMVitals, we would consider that a positive application of our guide.

Our Model (Redux)

Perhaps the biggest takeaway from the response to our  last blog post is that it is very difficult to give detailed feedback on a guide that is mostly whitespace. Below is an updated mock-up, which describes a set of RDM practices along the continuum of data management maturity. At present, we are not aiming to illustrate a full range of data management practices. More simply, this mock-up is intended to show the types of practices that could be described by our guide once it is complete.

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An updated visualization of our guide based on reader feedback. At this stage, the example RDM practices are intended to be representative not comprehensive.

Project Planning

The “Project Planning” stage describes practices that occur prior to the start of data collection. Our examples are all centered around data management plans (DMPs), but other considerations at this stage could include training in data literacy, engagement with local RDM services, inclusion of “sharing” in project documentation (e.g. consent forms), and project pre-registration.

Data Collection

The “Data Collection” stage describes practices related to the acquisition, accumulation, measurement, or simulation of data. Our examples relate mostly to standards around file naming and structuring, but other considerations at this stage could include the protection of sensitive or restricted data, validation of data integrity, and specification of linked data.

Data Analysis

The “Data Analysis” stage describes practices that involve the inspection, modeling, cleaning, or transformation of data. Our examples mostly relate to documenting the analysis workflow, but other considerations at this stage could include the generation and annotation of code and the packaging of data within sharable files or formats.

Data Output

The “Data Output” stage describes practices that involve the communication of either the data itself of conclusions drawn from the data. Our examples are mostly related to the communication of data linked to scholarly publications, but other considerations at this stage could include journal and funder mandates around data sharing, the publication of data papers, and the long term preservation of data.

Next Steps

Now that we’ve solicited a round of feedback from the community that works on issues around research support, data management, and digital curation, our next step is to broaden our scope to include researchers.

Specifically we are looking for help with the following:

  • Do you find the divisions within our model useful? We’ve used the research data lifecycle as a framework because we believe it makes our tool user-friendly for researchers. At the same time, we also acknowledge that the lines separating planning, collection, analysis, and output can be quite blurry. We would be grateful to know if researchers or data management service providers find these divisions useful or overly constrained.
  • Should there be more discrete “steps” within our framework? Because we view data management maturity as a continuum, we have shied away from creating discrete steps within each division. We would be grateful to know how researchers or data management service providers view this approach, especially when compared to the more quantitative approach employed by CARDIO, the Capability Profile Template, and DMVitals.
  • What else should we put into our model? Researchers are faced with changing expectations and obligations in regards to data management. We want our model to reflect that. We also want our model to reflect the relationship between research data management and broader issues like openness and reproducibility. With that in mind, what other practices and considerations should or model include?
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Building a user-friendly RDM maturity model

UC3 is developing a guide to help researchers assess and progress the maturity of their data management practices.

What are we doing?

Researchers are increasingly faced with new expectations and obligations in regards to data management. To help researchers navigate this changing landscape and to complement existing instruments that enable librarians and other data managers to assess the maturity of data management practices at an institutional or organizational level, we’re developing a guide that will enable researchers to assess the maturity of their individual practices within an institutional or organizational context.

Our aim is to be descriptive rather than prescriptive. We do not assume every researcher will want or need to achieve the same level of maturity for all their data management practices. Rather, we aim to provide researchers with a guide to specialist knowledge without necessarily turning researchers into specialists. We want to help researchers understand where they are and, where appropriate, how to get to where they want or need to be.

Existing Models

As a first step in building our own guide, we’ve researched the range of related tools, rubrics, and capability models. Many, including the Five Organizational Stages of Digital Preservation, the Scientific Data Management Capability Model, and the Capability Maturity Guide developed by the Australian National Data Service, draw heavily from the SEI Capability Maturity Model and are intended to assist librarians, repository managers, and other data management service providers in benchmarking the policies, infrastructure, and services of their organization or institution.  Others, including the Collaborative Assessment of Research Data Infrastructure and Objectives (CARDIO), DMVitals, and the Community Capability Framework, incorporate feedback from researchers and are intended to assist in benchmarking a broad set of data management-related topics for a broad set of stockholders – from organizations and institutions down to individual research groups.

We intend for our guide to build on these tools but to have a different, and we think novel, focus. While we believe it could be a useful tool for data management service providers, the intended audience of our guide is research practitioners. While integration with service providers in the library, research IT, and elsewhere will be included where appropriate, the the focus will be on equipping researchers to assess and refine their individual own data management activities. While technical infrastructure will be included where appropriate, the focus will be on behaviors, “soft skills”, and training.

Our Guide

Below is a preliminary mockup of our guide. Akin to the “How Open Is It?” guide developed by SPARC, PLOS, and the OASPA, our aim is to provide a tool that is comprehensive, user-friendly, and provides tangible recommendations.  

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Obviously we still have a significant amount of work to do to refine the language and fill in the details. At the moment, we are using elements of the research data lifecycle to broadly describe research activities and very general terms to describe the continuum of practice maturity. Our next step is to fill in the blanks- to more precisely describe research activities and more clearly delineate the stages of practice maturity. From there, we will work to outline the behaviors, skills, and expertise present for each research activity at each stage.

Next Steps

Now that we’ve researched existing tools for assessing data management services and sketched out a preliminary framework for our guide, our next step is to elicit feedback from the broader community that works on issues around research support, data management, and digital curation and preservation.

Specifically we are looking for help on the following:

  • Have we missed anything? There is a range of data management-related rubrics, tools, and capability models – from the community-focused frameworks described above to frameworks focused on the preservation and curation of digital assets (e.g. the Digital Asset Framework, DRAMBORA). As far as we’re aware, there isn’t a complementary tool that allows researchers to assess where they are and where they want to be in regards to data management. Are there efforts that have already met this need? We’d be grateful for any input about the existence of frameworks with similar goals.
  • What would be the most useful divisions and steps within our framework? The “three legged stool” developed by the Digital Preservation Management workshop has been highly influential for community and data management provider-focused tools. Though examining policies, resources, and infrastructure are also important for researchers when self-assessing their data management practices, we believe it would be more useful for our guide to be more reflective of how data is generated, managed, disseminated in a research context. We’d be grateful for any insight into how we could incorporate related models – such as those depicting the research data lifecycle – into our framework.
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Data (Curation) Viz.

Data management and data curation are related concepts, but they do not refer to precisely the same things. I use these terms so often now that sometimes the distinctions, fuzzy as they are, become indistinguishable. When this happens I return to visual abstractions to clarify —in my own mind—what I mean by one vs. the other. Data management is more straightforward and almost always comes in the guise of something like this:

The obligatory research data management life cycle slide. Everyone uses it, myself included, in just about every presentation I give these days. This simple (arguably oversimplified) but useful model defines more-or-less discrete data activities that correspond with different phases of the research process. It conveys what it needs to convey; namely, that data management is a dynamic cycle of activities that constantly influence one another. Essentially, we can envision a feedback loop.

Data curation, on the other hand, is a complex beastie. Standard definitions cluster around something like this one from the Digital Curation Centre in the UK:

Data curation involves maintaining, preserving, and adding value to digital research data throughout its lifecycle.

When pressed for a definition, this is certainly an elegant response. But, personally, I don’t find it to be helpful at all when I try to wrap my head around the myriad activities that go into curating anything, much less distinguishing management activities from curation activities. Moreover, I’m talking about all kinds of activities in the context of “data,” a squishy concept in and of itself. (We’ll go with the NSF’s definition: the recorded factual material commonly accepted in the scientific community as necessary to validate research findings.)

I suppose I should mention sooner or later that the point of defining “data” and all these terms appended to it is the following. There’s a lot of it [data] and we need to figure out what on earth to do with it, ergo the proliferation of new positions with these things “data management” and “data curation” in their titles. It’s important to make sure we’re speaking the same language.

There are other, more expansive approaches to defining data curation and a related post on this very blog, but to really grasp what I’m talking about when I’m saying the words “data curation,” I invariably come back to this visualization created by Tim Norris. Tim is a geographer turned CLIR Postdoctoral Fellow in Data Curation at the University of Miami. Upon assuming a new post with an unfamiliar title, he decided to draw a map of his job to explain (to himself and to others) what he means by data curation. Many thanks to Tim for sharing this exercise with the rest of our CLIR cohort and now with the blogo-world-at-large.

Below is an abbreviated caption, in Tim’s own words, as well as short- (3 min) and long-format (9 min) tours of the map narrated by Tim. And here is a handy PNG file for those occasions when the looping life cycle visualizations just won’t do.

This map of data curation has two visual metaphors. The first is that of a stylized mandala: a drawing that implies both inwards and outwards motion that is in balance. And the second is that of a Zen Koen: first there is a mountain, then there’s none, and then there is. We start with visual complexity—the mountain. To build the data curation mountain we start with a definition of the word “curation” as a five step process that moves inwards. The final purpose of this curation is to move what is being curated back into the world for re-use, publication and dissemination. This can be understood as stewardship. Next we think about the sources of data in the outside world. These sources have been abstracted into three data spaces: library digital collections, external data sources, and research data products. As this data moves “inwards” we can think of verbs that describe the ingestion processes. Metadata creation, or describing the data, is a key that enables later data linkages to be identified with the final goal of making data interoperable. Once the data is “inside” the curation space it passes through a standard process that begins with storage and ends with discovery. Specific to data in this process are the formats in which the data is stored and the difference between preservation and conservation for data. To enable this work we need hardware, software, and human interfaces to the curated data. Finally, as the data moves back out into the world, we must pay attention to institutions of property rights and access. If we get this all right we will have a system that is sustainable, secure, and increases the value of our research data collections. Once again we have a mountain.

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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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DataONE Funded by NSF for Another Round!

This is a week for important funding announcements! I already blogged about our new NSF funding for a Data-Level Metrics project with PLOS and DataONE, and on the heels of that, DataONE has announced their newest round of funding! I’ve borrowed heavily from their press release for this post. If you aren’t familiar with the work DataONE has been doing, go check out their website to learn more.

DataONE awarded $15 million from the NSF as part of an accomplishment based renewal.

DataONE: the Data Observation Network for Earth (www.dataone.org) is a distributed cyberinfrastructure that meets the needs of science and society for open, persistent, robust, and accessible Earth observational data. DataONE has dramatically increased the discoverability and accessibility of diverse yet interrelated Earth and environmental science data. In doing so, it has enhanced the efficiency of research and enabled scientists, policy makers and others to more easily address complex questions about our environment and our role within it.

DataONE Phase 1

Founded in 2009 by the NSF, DataONE was designed to provide both the tools and infrastructure for organizing and serving up vast amounts of scientific data, in addition to building an engaged community and developing openly available educational resources.

Accomplishments from the last five years include making over 260,000 publicly available data and metadata objects accessible through the DataONE search engine and building a growing network of 22 national and international data repositories. DataONE has published more than 74 papers, reached over 2,000 individuals via direct training events and workshops and connects with over 60,000 visitors annually via the website.

DataONE has developed an Investigator Toolkit that provides users with tools supporting activities across the full research data life cycle; a dynamic in-person and web-based education program comprising workshops, online best practices, curricula, training modules and other resources; and an engaged community of users via the DataONE Users Group and through collaboration with other national and international initiatives.

Plans for DataONE Phase 2

During the second phase, DataONE will target goals that enable scientific innovation and discovery while massively increasing the scope, interoperability, and accessibility of data. In particular DataONE will:

  • Significantly expand the volume and diversity of data available to researchers for large-scale scientific innovation;

  • Incorporate innovative features to dramatically improve data discovery and further support reproducible and open science; and

  • Establish an openly accessible online education series to support global participation and training in current techniques and perspectives.

DataONE will continue to engage, educate and grow the DataONE community, seek user input to ensure intuitive, user-friendly products and services, and work to ensure the long term sustainability of DataONE services so they continue to evolve and meet needs of researchers and other stakeholders for decades to come.

DataONE Phase 2 might not be quite as influential on graffiti culture as the 80s NY artist Phase 2. Click on the pic to learn more.

DataONE Phase 2 might not be quite as influential on graffiti culture as the 80s NY artist Phase 2. Click on the pic to learn more.

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It takes a data management village

A couple of weeks ago, information scientists, librarians, social scientists, and their compatriots gathered in Toronto for the 2014 IASSIST meeting. IASSIST is, of course, an acronym which I always have to look up to remember – International Association for Social Science Information Service & Technology. Despite its forgettable name, this conference is one of the better meetings I’ve attended. The conference leadership manages to put together a great couple of days, chock full of wonderful plenaries and interesting presentations, and even arranged a hockey game for the opening reception.

Yonge Street crowds celebrating the end of the Boer War, Toronto, Canada. This image is available from the City of Toronto Archives, and is in the public domain.

Although there were many interesting talks, and I’m still processing the great discussions I had in Toronto, a couple really rang true for me. I’m going to now shamelessly paraphrase one of these talks (with permission, of course) about building a “village” of data management experts at institutions to best service researchers’ needs. All credit goes to Alicia Hofelich Mohr and Thomas Lindsay, both from University of Minnesota. Their presentation was called “It takes a village: Strengthening data management through collaboration with diverse institutional offices.” I’m sure IASSIST will make the slides available online in the near future, but I think this information is too important to not share asap.

Mohr and Lindsay first described the data life cycle, and emphasized the importance of supporting data throughout its life – especially early on, when small things can make a big difference down the road. They asserted that in order to provide support for data management, librarians need to connect with other service providers at their institutions. They then described who these providers are, and where they fit into the broader picture. Below I’ve summarized Mohr and Lindsay’s presentation.

Grants coordinators

Faculty writing grants are constantly interacting with these individuals. They are on the “front lines” of data management planning, in particular, since they can point researchers to other service providers who can help over the course of the project. Bonus – grants offices often have a deep knowledge of agency requirements for data management.

Sponsored projects

The sponsored projects office is another service provider that often has early interactions with researchers during their project planning. Researchers are often required to submit grants directly to this office, who ensure compliance and focus on requirements needed for proposals to be complete.

College research deans

Although this might be an intimidating group to connect with, they are likely to be the most aware of the current research climate and can help you target your services to the needs of their researchers. They can also help advocate for your services, especially via things like new faculty orientation. Generally, this group is an important ally in facilitating data sharing and reuse.

IT system administrators

This group is often underused by researchers, despite their ability to potentially provide researchers with server space, storage, collaboration solutions, and software licenses. They are also useful allies in ensuring security for sensitive data.

Research support services & statistical consulting offices

Some universities have support for researchers in the designing, collecting, and analyzing of their data. These groups are sometimes housed within specific departments, and therefore might have discipline-specific knowledge about repositories, metadata standards, and cultural norms for that discipline. They are often formally trained as researchers and can therefore better relate to your target audience. In addition, these groups have the opportunity to promote replicable workflows and help researchers integrate best practices for data management into their everyday processes.

Data security offices, copyright/legal offices, & commercialization offices

Groups such as these are often overlooked by librarians looking to build a community of support around data management. Individuals in these offices may be able to provide invaluable expertise to your network, however. These groups contribute to and implement University security, data, and governance policies, and are knowledgeable about the legal implications of data sharing, especially related to sensitive data. Intellectual property rights, commercialization, and copyright are all complex topics that require expertise not often found among other data stewardship stakeholders. Partnering with experts can help reduce the potential for future problems, plus ensure data are shared to the fullest extent possible.

Library & institutional repository

The library is, of course, distinct from an institutional repository. However, often the institution’s library plays a key role in supporting, promoting, and often implementing the repository. I often remind researchers that librarians are experts in information, and data is one of many types of information. Researchers often underuse librarians and their specialized skills in metadata, curation, and preservation. The researchers’ need for a data repository and the strong link between repositories and librarians will change this in the coming years, however. Mohr and Lindsay ended with this simple statement, which nicely sums up their stellar presentation:

The data support village exists across levels and boundaries of the institution as well as across the lifecycle of data management.

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Git/GitHub: A Primer for Researchers

The Beastie Boys knew what’s up: Git it together. From egotripland.com

I might be what a guy named Everett Rogers would call an “early adopter“. Rogers wrote a book back in 1962 call The Diffusion of Innovation, wherein he explains how and why technology spreads through cultures. The “adoption curve” from his book has been widely used to  visualize the point at which a piece of technology or innovation reaches critical mass, and divides individuals into one of five categories depending on at what point in the curve they adopt a given piece of technology: innovators are the first, then early adopters, early majority, late majority, and finally laggards.

At the risk of vastly oversimplifying a complex topic, being an early adopter simply means that I am excited about new stuff that seems promising; in other words, I am confident that the “stuff” – GitHub, in this case –will catch on and be important in the future. Let me explain.

Let’s start with version control.

Before you can understand the power GitHub for science, you need to understand the concept of version control. 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. The Wikipedia 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. Enter Git. Git is a free, open-source distributed version control system, originally created for Linux kernel development in 2005. There are other version control systems– most notably, Apache Subversion (aka SVN) and Mercurial. However I posit that the existence of GitHub is what makes Git particularly interesting for researchers.

So what is GitHub?

GitHub is a web-based hosting service for projects that use the Git revision control system. It’s free (with a few conditions) and has been quite successful since its launch in 2008. Historically, version control systems were developed for and by software developers. GitHub was created primarily as a way for efficiently developing software projects, but its reach has been growing in the last few years. Here’s why.

Note: I am not going into the details of how git works, its structure, or how to incorporate git into your daily workflow. That’s a topic best left to online courses and Software Carpentry Bootcamps

What’s in it for researchers?

At this point it is good to bring up a great paper by Karthik Ram titled “Git can facilitate greater reproducibility and increased transparency in science“, which came out in 2013 in the journal Source Code for Biology and Medicine. Ram goes into much more detail about the power of Git (and GitHub by extension) for researchers. I am borrowing heavily from his section on “Use cases for Git in science” for the four benefits of Git/GitHub below.

1. Lab notebooks make a comeback. The age-old practice of maintaining a lab notebook has been challenged by the digital age. It’s difficult to keep all of the files, software, programs, and methods well-documented in the best of circumstances, never mind when collaboration enters the picture. I see researchers struggling to keep track of their various threads of thought and work, and remember going through similar struggles myself. Enter online lab notebooks. naturejobs.com recently ran a piece about digital lab notebooks, which provides a nice overview of this topic. To really get a feel fore the power of using GitHub as a lab notebook, see GitHubber and ecologist Carl Boettiger’s site. The gist is this: GitHub can serve as a home for all of the different threads of your project, including manuscripts, notes, datasets, and methods development.

2. Collaboration is easier. You and your colleagues can work on a manuscript together, write code collaboratively, and share resources without the potential for overwriting each others’ work. No more v23.docx or appended file names with initials. Instead, a co-author can submit changes and document those with “commit messages” (read about them on GitHub here).

3. Feedback and review is easier. The GitHub issue tracker allows collaborators (potential or current), reviewers, and colleagues to ask questions, notify you of problems or errors, and suggest improvements or new ideas.

4. Increased transparency. Using a version control system means you and others are able to see decision points in your work, and understand why the project proceeded in the way that it did. For the super savvy GitHubber, you can make available your entire manuscript, from the first datapoint collected to the final submitted version, traceable on your site. This is my goal for my next manuscript.

Final thoughts

Git can be an invaluable tool for researchers. It does, however, have a bit of a high activation energy. That is, if you aren’t familiar with version control systems, are scared of the command line, or are married to GUI-heavy proprietary programs like Microsoft Word, you will be hard pressed to effectively use Git in the ways I outline above. That said, spending the time and energy to learn Git and GitHub can make your life so. much. easier. I advise graduate students to learn Git (along with other great open tools like LaTeX and Python) as early in their grad careers as possible. Although it doesn’t feel like it, grad school is the perfect time to learn these systems. Don’t be a laggard; be an early adopter.

References and other good reads

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Abandon all hope, ye who enter dates in Excel

Big thanks to Kara Woo of Washington State University for this guest blog post!

Update: The XLConnect package has been updated to fix the problem described below; however, other R packages for interfacing with Excel may import dates incorrectly. One should still use caution when storing data in Excel.


Like anyone who works with a lot of data, I have a strained relationship with Microsoft Excel. Its ubiquity forces me to tolerate it, yet I believe that it is fundamentally a malicious force whose main goal is to incite chaos through the obfuscation and distortion of data.1 After discovering a truly ghastly feature of how it handles dates, I am now fully convinced.

As it turns out, Excel “supports” two different date systems: one beginning in 1900 and one beginning in 1904.2 Excel stores all dates as floating point numbers representing the number of days since a given start date, and Excel for Windows and Mac have different default start dates (January 1, 1900 vs. January 1, 1904).3 Furthermore, the 1900 date system purposely erroneously assumes that 1900 was a leap year to ensure compatibility with a bug in—wait for it—Lotus 1-2-3.

You can’t make this stuff up.

What is even more disturbing is how the two date systems can get mixed up in the process of reading data into R, causing all dates in a dataset to be off by four years and a day. If you don’t know to look for it, you might never even notice. Read on for a cautionary tale.

I work as a data manager for a project studying biodiversity in Lake Baikal, and one of the coolest parts of my job is getting to work with data that have been collected by Siberian scientists since the 1940s. I spend a lot of time cleaning up these data in R. It was while working on some data on Secchi depth (a measure of water transparency) that I stumbled across this Excel date issue.

To read in the data I do something like the following using the XLConnect package:

library(XLConnect)
wb1 <- loadWorkbook("Baikal_Secchi_64to02.xlsx")
secchi_main <- readWorksheet(wb1, sheet = 1)
colnames(secchi_main) <- c("date", "secchi_depth", "year", "month")

So far so good. But now, what’s wrong with this picture?

head(secchi_main)
##         date secchi_depth year month
## 1 1960-01-16           12 1964     1
## 2 1960-02-04           14 1964     2
## 3 1960-02-14           18 1964     2
## 4 1960-02-24           14 1964     2
## 5 1960-03-04           14 1964     3
## 6 1960-03-25           10 1964     3

As you can see, the year in the date column doesn’t match the year in the year column. When I open the data in Excel, things look correct.

excel_secchi_data

This particular Excel file uses the 1904 date system, but that fact gets lost somewhere between Excel and R. XLConnect can tell that there are dates, but all the dates are wrong.

My solution for these particular data was as follows:

# function to add four years and a day to a given date
fix_excel_dates <- function(date) {
    require(lubridate)
    return(ymd(date) + years(4) + days(1))
}

# create a correct date column
library(dplyr)
secchi_main <- mutate(secchi_main, corrected_date = fix_excel_dates(date))

The corrected_date column looks right.

head(secchi_main)
##         date secchi_depth year month corrected_date
## 1 1960-01-16           12 1964     1     1964-01-17
## 2 1960-02-04           14 1964     2     1964-02-05
## 3 1960-02-14           18 1964     2     1964-02-15
## 4 1960-02-24           14 1964     2     1964-02-25
## 5 1960-03-04           14 1964     3     1964-03-05
## 6 1960-03-25           10 1964     3     1964-03-26

That fix is easy, but I’m left with a feeling of anxiety. I nearly failed to notice the discrepancy between the date and year columns; a colleague using the data pointed it out to me. If these data hadn’t had a year column, it’s likely we never would have caught the problem at all. Has this happened before and I just didn’t notice it? Do I need to go check every single Excel file I have ever had to read into R?

And now that I know to look for this issue, I still can’t think of a way to check the dates Excel shows against the ones that appear in R without actually opening the data file in Excel and visually comparing them. This is not an acceptable solution in my opinion, but… I’ve got nothing else. All I can do is get up on my worn out data manager soapbox and say:

and-thats-why-excel


  1. For evidence of its fearsome power, see these examples.
  2. Though as Dave Harris pointed out, “is burdened by” would be more accurate.
  3. To quote John Machin, “In reality, there are no such things [as dates in Excel spreadsheets]. What you have are floating point numbers and pious hope.”
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