Data-Driven Exercises For Chemistry

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Author Guidelines
Contributions to this collection are encouraged from the chemical community within any curricular area where data-driven inquiry is deemed beneficial (including general, physical, analytical, inorganic, and biological chemistry).  As the collection grows, submissions from different areas will be categorized accordingly. 
Prospective JCE Data-Driven exercises should be sent to the editor and curator of this collection, W. Tandy Grubbs at  The highest priority for acceptance to this collection is given to exercises that provide ample opportunity for students to interact with the material and discover relevant chemical principles.  Submissions that take advantage of more contemporary literature data and which focus upon modern chemical topics are particularly encouraged.
Content and Formatting:
Submissions to JCE Data-Driven will include four items, formatted as follows:
  1. Abstract (submitted as a separate, ASCII document):  Title, author and author affiliation, and three to ten sentences that describe the data-driven exercise, the source of the data, and the intended audience (provide a recommendation of where the exercise might be used in the overall curriculum).  This statement will be abstracted by Chemical Abstracts.
  2. Data-Driven Exercise (submitted as a PDF or HTML document):  Details about the expected content and organization of the exercise are given below.  NOTE: if the exercise is based upon an extended numerical data set, please provide that data in a separate ‘PLAIN’ .txt file (containing only ASCII style data without descriptors; the file can be TAB delimited if the data is multi-columned).
  3. Completed copyright form:  This form can be found here.
  4. Five key words suitable for indexing the document in the Journal of Chemical Education index.  Suggested key words can be found here.
You are encouraged to look through posted submissions for guidance about the expected layout and content of JCE Data-Driven exercises.  The following general criteria should be kept in mind when designing your exercise:
  • The exercise should be based upon real-world data, particularly data that is valuable in terms of learning the concepts encountered in a 'modern' chemistry curriculum.  When literature data is utilized, credit should be given to the authors by referencing the source of the data.  Synthesized data is not permitted.
  • The exercise should be relatively short in duration (i.e. ideally, a student should be able to complete the assignment in one or, at most, two sessions). The theoretical background that precedes the exercised should be brief and include only those concepts that are central to the assignment.  
  • The prerequisites should be clearly stated.  Identify the topics that a student will need to have encountered in class or through independent study prior to attempting the exercise.  Clearly state whether the student will be expected to use a particular type of quantitative analysis software.  Specifically, identify whether the assignment can be done with a simple calculator, or whether the student needs to use a spreadsheet style software package (like Excel) or a symbolic mathematical software package (like MathCAD, Mathematica, or Maple)?
  • To promote brevity and make exercises usable by the broadest range of users, no attempt should be made to instruct students in the use of any particular software environment or computer platform, or how to otherwise accomplish common numerical methods.  The burden of providing this type of prerequisite training is placed on the instructor.
The data-driven exercise should be organized into the following sections (in the following order):
    1. Title
    2. Author and Author Affiliation
    3. Goals – a few sentences that state the phenomena that will be addressed and the scientific model(s) that will be employed during data analysis.
    4. Prerequisites - classroom concepts that should have been covered prior to attempting the exercise.
    5. Resources you will need - specify the quantitative analysis methods that will be employed (if any) and, therefore, the type of software that will be required. 
    6. Background - a concise, one or two page theoretical description that summarizes and, if necessary, expands upon the prerequisite concepts mentioned in (2) above.  Also review any uncommon numerical methods that may be utilized.
    7. Experimental Data - the data can be listed in tabular form or, if it is an extended data-set, a link can be included to a .txt file (make sure this .txt file is included as part of your submission).  Make sure to reference the data source.
    8. Exercise - outline a suggested protocol for analyzing the data and provide questions that the student should address.
You are welcome to contact the editor/curator of JCE Data-Driven, W. Tandy Grubbs, at with questions about the suitability and expected content and format of potential submissions.

Suggestions for improving this web site are welcome.  You are also encouraged to submit your own data-driven exercise to this web archive. All inquiries should be directed to the curator: Tandy Grubbs, Department of Chemistry, Unit 8271, Stetson University, DeLand, FL 32720.