Alyssa Goodman; Alberto Pepe; Alexander W. Blocker; Christine L. Borgman; Kyle Cranmer; Merce Crosas; Rosanne Di Stefano; Yolanda Gil; Paul Groth; Margaret Hedstrom; David W. Hogg; Vinay Kashyap; Ashish Mahabal; Aneta Siemiginowska; Aleksandra Slavkovic

“ The attribution system used for scholarly articles, accomplished via citations, often breaks in the case of data and software. When other authors reuse or cite your data or code, you may get an acknowledgment or an incoming link. If you and your colleagues have gone to the trouble to write a “data paper,” whose main purpose is to describe your data and/or code, you may also get a citation [8] . But, “data paper” writing is not always desirable, or relevant. So, how do you go about getting the full credit you deserve for your data and code? ”

“ The more the data created by your colleagues is accessible as an organized collection of some sort, the better your community's research capacity. The more data get shared, used, and cited, the more they improve. Besides personal involvement and encouragement, the best way to reward data sharing is by attribution: always cite the sources of data that you use. Follow good scientific practice and give credit to those whose data you use, following their preferred reference format and according to current best practices. ”

“ Computer and information scientists call the combination of the collection methods and analysis processes for a project its “workflow,” and they consider the information used and captured in the workflow to be part of the “provenance” of the data. In some cases (mostly in genomics) , scientists can use existing workflow software in running experiments and in recording what was done in those experiments, e.g., Gene Pattern (www.genepattern.org) . In that best-case scenario, the workflow software, its version, and settings used can be published alongside data using the other rules laid out here. ”

“ At the very least, loving your own available data will serve you: you'll be able to find and reuse your own data if you treat them well. Second, enable and encourage others to cherish, document, and publish their data. If you are a research scientist, chances are that not only are you an author, but also a reviewer for a specialized journal or conference venue. As a reviewer, request that the authors of papers you review provide documentation and access to their data according to the rules set out in the remainder of this article. ”

“ Today, most research projects are considered complete when a journal article based on the analysis has been written and published. The trouble is, unlike Galileo's report in Sidereus Nuncius, the amount of real data and data description in modern publications is almost never sufficient to repeat or even statistically verify a study being presented. ”

“ Foster and Use Data Repositories Sometimes the hardest and most time-consuming step of sharing data and code is finding and deciding where to put them. Data-sharing practices vary widely across disciplines: in some fields data sharing and reuse are essential and commonplace, while in others data sharing is a “gift exchange” culture [9] . If your community already has a standard repository, use it. ”

“ Make sure not only advanced researchers (e.g., postdocs) experience the pleasures of doing research with freely available data and tools: explain and show the value of well-loved data to graduate and undergraduate researchers. Teach whole courses, or mini-courses, related to caring for data and software, or incorporate the ideas into existing courses. Form groups specific to your discipline to foster data and code sharing. Hold birds-of-a-feather or special sessions during large meetings demonstrating examples in which good sharing practices have led to better results and collaborations. ”

“ Perfect documentation of provenance is rarely, if ever, attained in scientific work today. The higher the quality of provenance information, the higher the chance of enabling data reuse. In general, data reuse is most possible when: 1) data; 2) metadata (information describing the data) ; and 3) information about the process of generating those data, such as code, are all provided. ”

“ Eventually he used his observations to conclude that the Earth orbits the Sun, just as the four Galilean moons orbit Jupiter. History shows Galileo to be much more than an astronomical hero, though. His clear and careful record keeping and publication style not only let Galileo understand the solar system, they continue to let anyone understand how Galileo did it. Galileo's notes directly integrated his data (drawings of Jupiter and its moons) , key metadata (timing of each observation, weather, and telescope properties) , and text (descriptions of methods, analysis, and conclusions) . ”

“ Be a Booster for Data Science As Rule 1 says, it is important not just that you love your own data, but that others love data, too. An attitude that data and code are “second-class objects,” behind traditional scholarly publications, is still prevalent. ”

“ Source Code Repositories Github (http://github.com) : A web-based hosting service for software development projects that use the Git revision control system, including many open-source projects.# Git (http://git-scm.com) : A free and open-source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.# Mercurial (http://mercurial.selenic.com) : A free, distributed source control management tool. ”

“ It currently supports inclusion and rendering of d3.js plots and IPython notebooks.# Dexy (http://dexy.it) : A multipurpose project automation tool for working with documents via a command-line interface. ”