Second-named author

Valmont

I'm the second named author on a published scientific paper and I'm just wondering if it is acceptable practice to include it in a list of 'publications' on a CV? I didn't write the final paper but I did carry out the entire experiment by myself.

5uspect

Yes of course, it's perfectly fine. Typically in a list of publications you boldface your name making it easy to see.

Black Swan

Yes, 2nd named authors are listed on CVs, which notes their contribution to the research/publication. 3rd and 4th named authors can also be listed, but obviously the 1st named author gets lead credit.

loudona

Your position in an author list doesn't necessarily denote input into the publication due to political and social reasons behind author lists, unfortunately. So really you can put down anything on your CV which you consider yourself an important part of.

Black Swan

Citing 2 or more authors in scholarly writing is quite common. It occurs in CVs, bibliographies, references, and in-text citations.

For example, in APA writing style for in-text citations up to 5 authors in order of contribution are included:

(author 1, author 2, author 3, author 4, & author 5, 2014)

But after this first citation and in subsequent in-text citations those with 3 to 5 authors only show the first named author:

(author 1 et al., 2014)

And for those publications with 6 or more authors, in-text citations only show the first author:

(author 1 et al., 2014)

"It's nice to be king" first author! (220 Volt)

djpbarry

Valmont wrote: »

I'm the second named author on a published scientific paper and I'm just wondering if it is acceptable practice to include it in a list of 'publications' on a CV? I didn't write the final paper but I did carry out the entire experiment by myself.

If you did all the work then you really should have been first author.

Either way, I agree with everyone else - absolutely put it on your CV.

Black Swan

djpbarry wrote: »

If you did all the work then you really should have been first author.

Generally agree. In this case the 1st author drafted the manuscript, while the 2nd author did the research. It could be a toss up as to whom should be 1st and 2nd, depending upon the unique circumstances.

If the research and data are still relatively fresh, perhaps Valmont would consider drafting a second article for publication, taking a different perspective from the first publication shared (if feasible)? He could be 1st author, and perhaps only author. Then again, if Valmont thought that the author in the first publication could contribute to the second publication, then offer him/her 2nd author. When doing degree-related research, or non-degree related research, we try to get more than one publication from such researches; e.g., slicing and dicing the research to focus on different parts, or from different perspectives, etc.

EarnestH

Black Swan wrote: »

Generally agree. In this case the 1st author drafted the manuscript, while the 2nd author did the research. It could be a toss up as to whom should be 1st and 2nd, depending upon the unique circumstances.

If the research and data are still relatively fresh, perhaps Valmont would consider drafting a second article for publication, taking a different perspective from the first publication shared (if feasible)? He could be 1st author, and perhaps only author. Then again, if Valmont thought that the author in the first publication could contribute to the second publication, then offer him/her 2nd author. When doing degree-related research, or non-degree related research, we try to get more than one publication from such researches; e.g., slicing and dicing the research to focus on different parts, or from different perspectives, etc.

Yes, but this practice requires great care - and can be risky at times. A lot of journals now require an explanation if you are using the same data, even if you are creating different variables. In fact, I know of a journal in psychology that requires you to submit a list of all variables in your data set. And I know of several papers that have been retracted for slicing the salami too thinly.

Be careful.

Black Swan

We've sliced and diced both primary and secondary data sets in the past and published. This often happens when data mining secondary data sets collected by others. Of course, each publication states its requirements and should be followed accordingly.

I was surprised to have published an article in a peer-reviewed journal based upon pilot data, which had obvious and substantial limitations. The abstract and methodology clearly stated it was pilot data, and such limitations had been written in the conclusions of the manuscript, yet they still published it. Furthermore, the senior editor invited us to submit another manuscript after we collected the main study data, which we did, and got a 2nd publication.

EarnestH

You're blurring the issue, Black Swan. The issues of data mining and slicing and dicing are very different. Data mining is a practice of looking for statistically reliable effects, and then hypothesizing once the results are known. Although there are different forms of data mining, it typically involves exploring for effects without much preceding theory. I've seen some people run 100s if not 1000s of analyses to try to find such effects.

Slicing and dicing involves developing different papers with different (though sometimes overlapping) variables to increase publication output. While the practice is widespread, especially given the tenure system, a lot of disciplines and fields are now demanding more transparency. Statistically, the practice maybe damaging since slicing and dicing can result in an omitted variable bias that can confound your results.

So, bottom line, the advice offered by Black Swan is misguided. That is not to say that we should be limited to one publication per data set, but rather than we avoid splitting these data sets just to get another (probably low impact) publication.

And as to the question of publishing a pilot, I see that as a very questionable editorial policy given that effects will change as sample size increases.

Black Swan

EarnestH wrote: »

Data mining is a practice of looking for statistically reliable effects, and then hypothesizing once the results are known.

We do not call what emerges from an inductive exploration of secondary data sets "hypothesizing." Hypotheses are deductions from theory, not inducting from data mining. Patterns that emerge from an exploration of secondary data are called empirical generalizations used inform theory revision or creation. Empirical generalizations are not hypotheses. Although I may be simplifying Wallace's Wheel of Science, what you stated (data mining > hypothesizing) was very misleading, if not in some cases spurious.

From a practical standpoint, if our client was not well versed in research (e.g., many business CEOs), they could care less about theories or hypothesizing, and if we put that in our executive summaries and supporting documentation after data mining, odds are we would lose their future business (e.g., I was at a Private Industry Counsel short-list RFP meeting where an academic approach to data mining was being presented, only to see them cut-off in mid-sentence without funding).

EarnestH wrote: »

And as to the question of publishing a pilot, I see that as a very questionable editorial policy given that effects will change as sample size increases.

The research topic was new and of interest to their readership. The pilot was treated as exploratory (in addition to being used to standardize the methodology, data collection, analysis, etc.). The small pilot data size was not intended to be representative, generalizable, explain or predict, only to lay the groundwork for the main study. It was a PILOT. That was made obvious in my post above.

This had been completely transparent in both the pilot and main study manuscripts. Both the Sr Editor and two different sets of reviewers were mostly positive with a few suggested revisions before publication. These two publications provided support for the RFP that I replied/drafted to research the topic further, and the grant monies were awarded.

Research follows money. The RFPs have been extremely competitive, especially since the Great Recession. Our two publications provided support for a continued revenue stream, so what you may think of the "editorial policy" of the peer-reviewed journal that published is of no concern to us.

EarnestH

Black Swan wrote: »

We do not call what emerges from an inductive exploration of secondary data sets "hypothesizing." Hypotheses are deductions from theory, not inducting from data mining. Patterns that emerge from an exploration of secondary data are called empirical generalizations used inform theory revision or creation. Empirical generalizations are not hypotheses. Although I may be simplifying Wallace's Wheel of Science, what you stated (data mining > hypothesizing) was very misleading, if not in some cases spurious.

From a practical standpoint, if our client was not well versed in research (e.g., many business CEOs), they could care less about theories or hypothesizing, and if we put that in our executive summaries and supporting documentation after data mining, odds are we would lose their future business (e.g., I was at a Private Industry Counsel short-list RFP meeting where an academic approach to data mining was being presented, only to see them cut-off in mid-sentence without funding).

The research topic was new and of interest to their readership. The pilot was treated as exploratory (in addition to being used to standardize the methodology, data collection, analysis, etc.). The small pilot data size was not intended to be representative, generalizable, explain or predict, only to lay the groundwork for the main study. It was a PILOT. That was made obvious in my post above.

This had been completely transparent in both the pilot and main study manuscripts. Both the Sr Editor and two different sets of reviewers were mostly positive with a few suggested revisions before publication. These two publications provided support for the RFP that I replied/drafted to research the topic further, and the grant monies were awarded.

Research follows money. The RFPs have been extremely competitive, especially since the Great Recession. Our two publications provided support for a continued revenue stream, so what you may think of the "editorial policy" of the peer-reviewed journal that published is of no concern to us.

First of all, I have got nothing against data mining, so long as such exploratory efforts are not used to test theory, and provided the process is transparent. Granted, such generalizations, as you or your friend Wallace calls them, can be helpful in the research process to discover anomalies. But in my experience data mining takes on a far shadier form - and is used for very different purposes.

Second, no where in your post have you addressed the practice you are professing in terms of slicing and dicing data sets. I raised a legitimate concern that such practices run the risk of an omitted variable bias which can confound results. Are you suggesting that this is not a concern? Or that in the greater scheme of creating a revenue stream, just not important?

Finally, you don't have to justify publishing a pilot to me or anybody else; we all choose our own paths. However, as a matter of curiosity, what is the impact factor of the journal in which the pilot study is published? I'd have a hard time imagining that it was published in a high impact journal, but my view is always open to empirical falsification.

Black Swan

EarnestH wrote: »

Second, no where in your post have you addressed the practice you are professing in terms of slicing and dicing data sets.

Have you ever used secondary data sets (i.e., data collected by others), and published results? Have others also accessed this same secondary data set and also published results? Ever heard of a secondary data set called the US Census? How many publications by one or more researchers occur out of this one secondary data set, and appear in high impact peer-reviewed journals? Enough.

EarnestH

Black Swan wrote: »

Have you ever used secondary data sets (i.e., data collected by others), and published results? Have others also accessed this same secondary data set and also published results? Ever heard of a secondary data set called the US Census? How many publications by one or more researchers occur out of this one secondary data set, and appear in high impact peer-reviewed journals? Enough.

Of course large public databases are used over and over, and most times this is perfectly legitimate. But this is not necessarily slicing and dicing since such databases contain a large variety of different variables which can be used as surrogates for numerous purposes. Perfectly legitimate. But not what you suggested. The idea of slicing and dicing is narrowing the focus of a study in the interest of getting a second study out of the remaining data. The result is often (not always) a confounded set of results that lack utility. The problem is not the action per se but the intent - publication rather than advancing knowledge. How quickly we cannot forget that journals are a communication system.

So, then can we assume that the impact factor of pilot study journal is quite low?

Black Swan

EarnestH wrote: »

Of course large public databases are used over and over, and most times this is perfectly legitimate. But this is not necessarily slicing and dicing since such databases contain a large variety of different variables which can be used as surrogates for numerous purposes. Perfectly legitimate. But not what you suggested.

That's your opinion. We can agree to disagree.

EarnestH wrote: »

So, then can we assume that the impact factor of pilot study journal is quite low?

You can assume anything you like. Both you and I have gone way off topic, which does not inform the discussion of the original "Second-named author" subject, which appears to have been answered earlier.