h index phd student

What Is Good H-Index? H-Index Required For An Academic Position

In the academic world, the h-index score stands as a pivotal metric, gauging the impact and breadth of a researcher’s work. Understanding what constitutes a good h-index is crucial for academics at all stages, from budding PhD students to seasoned professors.

This article looks into the h-index, exploring what scores are considered impressive across various disciplines and career stages.

• PhD Student: An h-index between 1 and 5 is typical for PhD students nearing the end of their program, reflecting their early stage in academic publishing.
• Postdoc and Assistant Professor: Early career researchers like postdoctoral fellows or assistant professors often find an h-index around 5 to 10 impressive, indicating a solid start in their respective fields.
• Associate Professor: At this more advanced stage, an h-index of 10 or more is generally expected, reflecting a consistent record of impactful research.
• Full Professor: For full professors, an h-index of 15 or higher is often seen, indicating a long and impactful career in research and academia.

How To Calculate Your H-Index Score?

In the academic world, the h-index score is a critical metric, essentially acting like a report card for scholars.

The h-index is a measure of a researcher’s productivity and impact. H-index was designed to assess the number of papers published and the number of citations each paper receives. 

Now that you know what is a h-index score, you may now wonder if you can find out your own. Good thing is that platforms like Google Scholar or Web of Science can come in handy.

They track your number of publications and the number of times those publications are cited, crunching these numbers into your h-index.

This number can vary based on the field and years of research experience. A full professor might be expected to have a higher h-index, reflecting more years of impactful research.

Google Scholar

To find out your h-index score from Google Scholar, you can follow the steps below:

• Create a Google Scholar Profile : If you don’t already have one, go to Google Scholar and create a profile. Fill in your academic details and affiliations.
• Add Publications : Ensure all your research publications are listed in your profile. You can add them manually or import them if they are already available on Google Scholar.
• Verify your Publications : Make sure the publications listed are indeed yours, as sometimes publications from other authors with similar names might appear.
• Check the Citations Section : Once your profile is complete and updated, look for the ‘Citations’ section on your profile page. This is usually located at the top and easy to spot.
• Find Your H-Index : In the Citations section, you will see your h-index listed among other citation metrics like the total number of citations and the i10-index.

Web Of Science

To find out your h-index score from Web Of Science, you can follow the steps below:

• Access Web of Science : Go to the Web of Science website. Access may require an institutional login, depending on your affiliation.
• Search for Your Name : Use the author search function to find your publications. Ensure you search with variations of your name if you’ve published under different names or initials.
• Create a Citation Report : Once your publications are listed, select them and create a citation report. This option is typically found above the list of your publications.
• View Your H-Index : In the citation report, your h-index will be displayed. This number is calculated based on the total number of papers you’ve published and the number of citations each paper has received.

What H-Index Is Considered Good For A PhD Student?

For a PhD student, the world of academic metrics can be daunting, especially when it comes to the h-index, a measure that intertwines the number of publications with their citation impact.

So, what h-index score should you, as a PhD student, aim for?

A “good” h-index can vary based on your field of study and the stage of your PhD program.

Generally, for PhD students, a lower h-index is expected and completely normal. You’re just beginning your journey in academic publishing.

An h-index between 1 and 5 might be typical for students nearing the end of their PhD. This means you have 1 to 5 publications that have been cited at least 1 to 5 times, respectively.

Your h-index can be calculated using tools like Google Scholar or Web of Science. These platforms track your published papers and the number of citations each receives.

As a PhD student, your focus should be on publishing quality research in reputable journals, as this will gradually increase your h-index.

Remember, while a higher h-index is beneficial for future academic positions, it’s not the only metric that matters. Your research’s quality, relevance, and impact in your field are equally important. A single highly influential paper might open more doors than several less impactful ones.

What Are Good H-Index Required For An Academic Position?

your h-index can be as crucial as your research itself. This metric, a blend of productivity and impact, is often scrutinized by hiring committees.

But what number should you aim for? A good h-index varies by field and career stage.

PostDoc, Assistant Professors

For early career researchers, like postdoctoral fellows or assistant professors, an h-index around 5 to 10 is often impressive.

It shows you’ve made a mark in your field, with a number of papers that have been cited at least that many times. 

Associate Professor, Full Professor

In more senior roles, such as a tenured associate professor or full professor, expectations rise.

Here, an h-index of 10 or 15 might be the minimum, with higher numbers not uncommon.

This single number, while important, doesn’t tell the whole story. A young researcher might have a lower h-index simply due to less time in the field. Moreover, some fields tend to have higher citation rates, which can inflate h-index scores.

It’s wise to keep an eye on your h-index, especially if you’re eyeing:

• Competitive academic positions,
• Research funding
• Collaboration opportunities.

Improving your h-index involves not just publishing papers, but ensuring they are of high quality and relevance, increasing the likelihood of citations.

In sum, a good h-index is one that matches your career stage and field, reflecting both the quantity and impact of your work. However, it’s not the sole measure of your worth as a researcher.

The breadth and depth of your contributions, beyond just citation counts, also paint a vivid picture of your academic and scientific impact.

What Metric Influences H-Index Score?

Your h-index score is influenced by several key factors:

• Number of Publications : The more papers you publish, the greater the potential for citations. It’s a numbers game, but quality over quantity should be your mantra. High-caliber papers in respected journals often garner more attention and citations.
• Citations Per Publication : Your h-index heavily relies on how often your papers are cited. Even if you have a plethora of publications, your h-index won’t shine if they’re seldom cited.
• Years of Research Experience : A young researcher might have a lower h-index compared to a full professor, who has had more time to build their citation record.
• Research Field : The h-index varies widely across disciplines. Fields with rapid publication and citation rates like biomedical sciences often see higher h-index scores than, say, humanities. So, a good h-index in one field might be considered low in another.
• Access to Research Collaborations : Collaborations can boost your h-index. Working with other researchers, can increase the visibility and citation potential of your papers. However, too many authors on a single paper might dilute the perceived contribution of each.

Remember, while a high h-index can be indicative of a significant academic impact, it’s not the sole measure of your scientific worth. It’s a good idea to give your h-index some consideration, but also focus on the broader spectrum of your academic contributions.

How To Increase H-Index Score?

Increasing your h-index, a metric reflecting the impact and productivity of your academic work, is a strategic goal for many researchers.

This single number, representing the intersection of the quantity of your publications and their citation impact, can play a pivotal role in securing research grants and academic positions.

To boost your h-index, focus on publishing quality research in well-regarded journals. A paper published in a respected journal is more likely to be cited, and each citation nudges your h-index upwards.

For example, if you’re an assistant professor with an h-index of 5, aiming for journals with high visibility in your field can help you reach a higher h-index, making you more competitive for positions like associate or full professor.

Collaboration is another key strategy. Co-authoring with established researchers can increase the reach and citation potential of your papers.

This, however, comes with a caveat: the more number of authors on a paper, the more diluted your perceived contribution might be. Aim for a balance in co-authorship.

Active engagement in the academic community also matters. Increase citations on your work by:

• Presenting at conferences,
• networking, and
• promoting your work on platforms like Google Scholar or Web of Science.

Remember, the h-index varies by field and career stage. A good h-index for a young researcher might be 10, while more senior academics might aim for higher numbers. Using databases like Google Scholar, you can track your number of cited publications and calculate your h-index.

While a higher h-index can bolster your academic profile, it’s not the sole indicator of your scholarly worth – low h-index score is not a dealbreaker in many cases. It’s wise to consider it alongside other measures of your academic and scientific impact.

Good H-Index Score May Vary

A good h-index score is relative, varying across academic fields and career stages. While it offers a valuable snapshot of a researcher’s impact and productivity, it’s important to view it as one part of a larger picture.

Aspiring for a higher h-index should go hand in hand with maintaining the quality and relevance of research. Ultimately, the h-index is a useful tool, but it’s the depth and innovation of your work that truly define your academic legacy.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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What is the h-index?

A simple definition of the h-index

Step-by-step outline: how to calculate your h-index, why it is important for your career to know about the h-index, can all your academic achievements be summarized by a single number, frequently asked questions about h-index, related articles.

An h-index is a rough summary measure of a researcher’s productivity and impact. Productivity is quantified by the number of papers, and impact by the number of citations the researchers' publications have received.

The h-index can be useful for identifying the centrality of certain researchers as researchers with a higher h-index will, in general, have produced more work that is considered important by their peers.

The h-index was originally defined by J. E. Hirsch in a Proceedings of the National Academy of Sciences article as the number of papers with citation number ≥ h . An h-index of 3 hence means that the author has published at least three articles, of which each has been cited at least three times.

The h-index can also simply be determined by charting the article's citation counts. The h-index is then determined by the interception of the chart's diagonal with the citation data. In this case, there are 3 papers that are above the diagonal, and hence the h-index is 3.

The definition of the h-index comes with quite a few desirable features:

• First, it is relatively unaffected by outliers. If e.g. the top-ranked article had been cited 1,000 times, this would not change the h-index.
• Second, the h-index will generally only increase if the researcher continues to produce good work. The h-index would increase to 4 if another paper was added with 4 citations, but would not increase if papers were added with fewer citations.
• Third, the h-index will never be greater than the number of papers the author has published; to have an h-index of 20, the author must have published at least 20 articles which have each been cited at least 20 times.
• Step 1 : List all your published articles in a table.
• Step 2 : For each article gather the number of how often it has been cited.
• Step 3 : Rank the papers by the number of times they have been cited.
• Step 4 : The h-index can now be inferred by finding the entry at which the rank in the list is greater than the number of citations.

Here is an example of a table where articles have been ranked by their citation count and the h-index has been inferred to be 3.

Luckily, there are services like Scopus , Web of Science , and Google Scholar that can do the heavy lifting and automatically provide the citation count data and calculate the h-index.

The h-index is not something that needs to be calculated on a daily basis, but it's good to know where you are for several reasons. First, climbing the h-index ladder is something worth celebrating. If it's worth opening a bottle of champagne or just getting a cafe latte, that's up to you, but seriously take your time to celebrate this achievement (there aren't that many in academia). But more importantly, the h-index is one of the measures funding agencies or the university's hiring committee calculate when you apply for a grant or a position. Given the often huge number of applications, the h-index is calculated in order to rank candidates and apply a pre-filter.

Of course, funding agencies and hiring committees do use tools for calculating the h-index, and so can you.

It is important to note that depending on the underlying data that these services have collected, your h-index might be different. Let's have a look at the h-index of the well-known physicist Stephen W. Hawking to illustrate it:

So, if you are aware of a number of citations of your work that are not listed in these databases, e.g. because they are in conference proceedings not indexed in these databases, then please state that in your application. It might give your h-index an extra boost.

➡️ Learn more: What is a good h-index?

Definitely not! People are aware of this, and there have been many attempts to address particular shortcomings of the h-index, but in the end, it's just another number that is meant to emphasize or de-emphasize certain aspects of the h-index. Anyway, you have to know the rules in order to play the game, and you have to know the rules in order to change them. If you feel that your h-index does not properly reflect your academic achievements, then be proactive and mention it in your application!

An h-index is a rough summary measure of a researcher’s productivity and impact . Productivity is quantified by the number of papers, and impact by the number of citations the researchers' publications have received.

Google Scholar can automatically calculate your h-index, read our guide How to calculate your h-index on Google Scholar for further instructions.

Even though Scopus needs to crunch millions of citations to find the h-index, the look-up is pretty fast. Read our guide How to calculate your h-index using Scopus for further instructions.

Web of Science is a database that has compiled millions of articles and citations. This data can be used to calculate all sorts of bibliographic metrics including an h-index. Read our guide How to use Web of Science to calculate your h-index for further instructions.

The h-index is not something that needs to be calculated on a daily basis, but it's good to know where you are for several reasons. First, climbing the h-index ladder is something worth celebrating. But more importantly, the h-index is one of the measures funding agencies or the university's hiring committee calculate when you apply for a grant or a position. Given the often huge number of applications, the h-index is calculated in order to rank candidates and apply a pre-filter.

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Why I love the H-index

The H-index – a small number with a big impact.  First introduced by Jorge E. Hirsh in 2005, it is a relatively simple way to calculate and measure the impact of a scientist (Hirsch, 2005). It divides opinion.  You either love it or hate it. I happen to think the H-index is a superb tool to help assess scientific impact.  Of course, people are always favourable towards metrics that make them look good.  So let’s get this out into the open now, my H-index is 44 (I have 44 papers with at least 44 citations) and, yes, I’m proud of it! But my love of the H-index stems from a much deeper obsession with citations.

As an impressionable young graduate student, I saw my PhD supervisor regularly check his citations.  Citations to papers means that someone used your work or thought it was relevant to mention in the context of their own work.  If a paper was never cited, and perhaps therefore also little read, was it worth doing the research in the first place? I still remember the excitement of the first citation I ever received and I still enjoy seeing new citations roll in.

The H index: what does it mean, how is it calculated and used?

The H-index measures the maximum number of papers N you have, all of which have at least N citations. So if you have 3 papers with at least 3 citations, but you don’t have 4 papers with at least 4 citations then your H-index is 3. Obviously, the H-index can only increase if you keep publishing papers and they are cited.  But the higher your H-index gets, the harder it is to increase it.

One of the ways in which I use the H-index is when making tenure recommendations. By placing the candidate within the context of the H-indices of their departmental peers, I can judge the scientific output of the candidate within the context of the host institution. This is a useful because it can be difficult to understand what is required at different host institutions from around the world.  It would be negligent to only look at H-index and so I use a range of other metrics as well,  together with good old fashioned scientific judgement of their contributions from reading their application and papers.

The m value

One of those extra metrics I use was also introduced by Hirsch, and is called m (Hirsch, 2005). M measures the slope or rate of increase of the H-index over time and is, in my view, a greatly underappreciated measure.   To calculate the m -value, take the researchers H-index and divide by the number of years since their first publication. This measure helps to normalise between those at the early or twilight stages of their career. As Hirsch did for physicists in the field of computational biology, I broadly categorise people according to their m value in the table below.  The boundaries correspond exactly to those used by Hirsch.

So post-docs with an m -value of greater than three are future science superstars and highly likely to have a stratospheric rise. If you can find one, hire them immediately!

The H-trajectory

The graph below shows the growth of the H-index for three scientists  – A, B and C – who respectively have an H-index of 12, 15 and 16.  I call these curves a researcher’s H-trajectory.

If we calculate their m -value, then we find that A has a value of 0.5, B has 0.94 and C a value of 1.67. So while each of these researchers has a similar H-index, their likelihood for future growth can be predicted based on past performance. Recently, Daniel Acuna and colleagues presented a sophisticated prediction of future H-index using a number of several features, such as number of publications and the number in top journals (Acuna et al . 2012).

As any serious citation gazer knows, the H-index has numerous potential problems. For example, researcher A who spent time in industry has fewer publications, people with names in non English alphabets or very common names can be difficult to correctly calculate, different fields have widely differing authorship, publication and citation patterns. But even considering all these problems, I believe the H-index is here to stay.  My experience is that ranking scientists by H-index and m-value correlates very well with my own personal judgements about the impact of scientists that I know and indeed with the positions that those scientists hold in Universities around the world.

Alex Bateman is currently a computational biologist at the Wellcome Trust Sanger Institute where he has led the Pfam database project. On Novembert 1st, he takes up a new role as  Head of Protein Sequence Resources at the EMBL-European Bioinformatics Institute (EMBL-EBI).

J.E. Hirsch. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102, 16569-16572.

D.E. Acuna, S. Allesina & P. Konrad. Predicting scientific success. Nature 489, 201-202.

The problem with the H factor is that it is, to a considerable extent, a measure of how old you are. The m index is supposed to correct this but it can distort things by assuming a linearity that just isn’t there in the development of a scientist.

The alternative I propose is the H5Y factor. It is the H factor, but calculated only on citations received in the past five years. This equalizes the playing field and my guess is that it is a much better predictor of performance for the next five years than H or m. Who cares what you have published thirty years ago? (unless it is still being cited, of course!)

I agree with Constantin, and would add that the m-index is particularly unfair to those who take early career breaks, since it takes several years before the penalty of having a gap after their first few papers starts to become trivially small.

Interestingly, Google Scholar’s “My Citations” pages (e.g. see my profile link for a not-so-random example!) does calculate what Constantin proposes, an H-index computed from the last five years’ citations, though they don’t call it H5Y. Personally, though I agree that this is better than H, I still think it’s a rather biased measure of quality, which more strongly reflects quantity or length of active career.

Funnily enough, I think Google are already using a better measure (which they call H5), but only for their journal rankings, not their author profiles, see e.g. scholar.google.co.uk/citations?view_op=top_venues This measure is the H-index for work published in the last five years, rather than just cited in the last five years, and they call this H5.

I think it would be great if Google Citations profiles showed H5 for authors, but frustratingly, Google’s FAQ indicates that they are opposed to adding new metrics: http://scholar.google.com/intl/en/scholar/citations.html#citations But perhaps Scopus, ResearcherID, Academia.edu, ResearchGate or similar will add H5 in the future…

[…] Crushing it Among Nobel Science Winners Why I love the H-index (not sure I’m afraid or in agreement) A Simple Way to Reduce the Excess of Antibiotics […]

Most of the H-trajectory plots that I have created for active scientists do show quite a linear trend. I only showed three in my graph above, but researcher A was the only significant deviation that I found. Creating these H-trajectory plots was not as easy as I thought it was going to be. Downloading the full citation data is time consuming given the limits imposed by SCOPUS and ISI. I also found that the underlying data for citations was not nearly as clean as I expected.

I agree that it is important to be able to take account of career breaks so that we do not penalise researchers unfairly. Being able to plot the H-trajectory might help spot these. But as I mentioned in the article these metrics should only be used as part of a wider evaluation of individuals outputs. I tend to agree with the google view on the proliferation of metrics that this could lead to more confusion than it solves. But H5-like measures seem like another reasonable way to normalise out the length of career issue.

I find Google Scholar far better than ISI. It is updated more regularly and gives better representation to publications in non-English journals. I would choose it over others to calculate any sort of index.

Ok, but why would the h-index given by an online calculator ever be higher than the number of publications?

I agree with Alex. I had the same experience, whether to recruit post-docs, young group leaders or evaluate tenure (and even in one case head of large institute). After 3, 10, 20 or 30 years of research, the h-index and m numbers are very good to evaluate not only the brilliance at one point, but also the steady success. You do not hire the genious who had only one magic paper and nothing else significant. The likelyhood that the magic happens again is very low. You have to compare with peers though. Having been an experimental neuroscientist and a computational modeller I know that the citation patterns are quite different. However, when using the H-index to compare people, we are generally in a situation where we compare similar scientists.

All that of course being a way to quickly sort out A, B or C lists, and uncovering potential problems (100 publications and h-index of 10). After that step, you need to evaluate the candidates more attentively, using interviews etc. But interestingly you very rarely read the publications. In the first screen you have too many of them and in the second you do not need them anymore.

(and I am “excellent” yeah! Not “stellar” though. One delusion I have to get rid of 😉 )

Interesting logic exercises. What about superstars who translate their work into patents/products and can not publish due company confidentiality, company goals, etc? Patents are not cited anywhere close to publications. Organic journals often have low impact factors and low citation rates as the animal studies in higher impact journals always overshadow the original synthetic papers. A good friend of mine has an H-index of only 7 but has designed a block-buster drug (and several other promising leads)–I would trade my inflated H-Index (product of a hot, speculative field) in a minute to have his stock options–oh and that drug that helps tens of thousands everyday. Sorry to burst that bubble–H-Indexers. Used to be a believer but I have now seen the light. Yep–and I would also take that “flash in the pan” invention of PCR (and the Nobel) over 50 years of high citations. One flash can have a greater impact than a thousand scientists over a thousand years.

[…] is reading about the H-index from someone with a big one. Does side matter? When it comes to how many times your works get cited it […]

Thanks for the helpful discussion. I just googled “what is a good h-index” and yours was the first thing to come up. I think as with any single statistic it has limitations, but overall is a decent reflection of output, especially for comparison with similar applicants for a position.

I’d convert your m index based on h-index/(fte years working since first paper) which would take account of breaks/part-time working. This would particularly help women remain competitive in the context of extended periods of part-time working. So my 10 years since 1st paper would turn into 10-1-(5*0.6) = 6, so my m index is 2 = 12/6 instead of 12/10. Woo!

Anyway I don’t think anything will stop me checking my citations obsessively and google citations is the easiest place I’ve found to keep my publications organised.

Amen. Coming from someone who was stuck too long in a company in which publishing in the open domain was a big no-no. H-index is one number, but it is not _the_ number. Neither are Google’s variations, and so forth. For example, IQ is another number, it has it uses, but it clearly isn’t _the_ number either. Me not like metrics so much.

I like the m-value, but it has the unfortunate effect of penalising the early starter. For example, someone who publishes a paper from their Hons thesis may be penalised by 3-4 years in the denominator producing their m-value when compared with someone starting publishing in the third or fourth year of their PhD. So I would take Kate’s idea further, and use FTE as THE denominator when calculating m, instead of years since first paper. This could include time spent as a PhD student, or not, as long as it was standardised.

Google Scholar gives exactly this statistic under the standard h-value.

One problem with H or M index can be how many people are actively involved in the research in a particular field. For example, there are only ~186 laboratories in the whole world working on my previous field, Candida albicans. But, right now I am working on cancer biology. Huge number of people are working in that field, hence the h index will increase dramatically.

I think that is a good suggestion. It is important to take account of career breaks when judging peoples scientific output. Its not perfect to just subtract the break length or some combination of time. Even during a career break your pre-break papers will still be cited and potentially increasing your H-index. But to a first approximation what you suggest makes good sense. It would be interesting to look at the H-trajectories of people who have taken a career break to see how it affects growth of H-index.

Yes that is a good point. Publishing a paper during your degree should be seen as a strong positive indicator in my opinion and as you say not penalise the person. OK, so lets use years of FTE employment as the denominator.

It is best to only using H-index for comparing people within the same field. I’m not sure that moving field is any guarantee of increasing H-index, but it will be easier for your H-index to grow in the large field. I guess the smart thing to do is to start in cancer biology then move to the specialist field 😉

I don’t like h index when it is used to rank journals as it basically gives a statistics about the best papers in that journal. For example if nature has a 5 year h index of 300, it only says something about those 300 papers and nothing about the thousands of other papers they published. Because of that, plos one has a very high h index, I think ranked top 30, but that just reflects the number of elite papers being published there, not the tens of thousands of junk papers it publishes..

My problem with h-index for insividuals is that it does not differentiate first author papers from contributing author papers. A tech could be put in 50 high impact papers over 5-6 years because he is in a super high impact lab for technical contributions. However a post doc in such a lab would have much fewer papers because be would be focusing on making first author papers. However in the end, the tech would have a higher h index. Is that a fair assessment? Also that tech could be a postdoc in name but doing tech work. Would such a technician postdoc be at a higher advantage to employers who only look at h index?

The extreme scenarios given to discount H-index can be absurd: a) A tech having 50 papers! I do not know a tech that is put on 50 papers in 5 years in any lab. If such a tech exists, then he/she is a superstar tech and needs to be celebrated. b) why penalize someone who publishes in their PhD with an m index- well you forget that if someone publishes early, then their h index will increase because their papers will start collecting citations early so even if the denominator is increased by a few years, isn’t the numerator also increased? c) In chemistry, there was a table of the top 500, based on h-index. All on that table were superstars, by other metrics, and all the recent nobel prize winners were on that list. There was not a single name on that list who was not famous. I agree that one can not use h index to different between h of say 15 and 20. But if someone has an h of say 60 and the other has 30, there is usually a light and day between them. The h is here to stay.

None of the above addresses key weaknesses of the h-index – self citation and citation rings.

If you work in large collaborations and projects it is *easy* for *many* people to rack up large numbers of citations (and h-indices) by citing each others papers and by simply appearing on lots of papers for which they have done little work. At the very least I believe citations should be a conserved quantity – one citation is one citation, and if it to is a paper with 100 authors then it should not add 1 citation to *each* of those author’s records, it should add 0.01 (or some other agreed fraction dictated by author order such that Sigma (fraction) =1).

Then, self-citations, both in the form of you citing your own paper, or any papers upon which a co-author appears citing that paper should not count.

This would cut many h-indices down to size and be a much truer reflection of an individual’s contribution.

What’s your normalised (by number of authors) h-index, excluding self-citations?

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Out lab publishes over 40 papers a year and has three techs contributing to almost every paper for technical work. They have higher h then postdocs.

But what is worse are PIs who do no work and don’t even read the paper but is still on the author list…apparently a common occurrence for high energy physics consortiums.

[…] h-index weaknesses with various computational models that, for example, reward highly-cited papers, correct for career length, rank authors’ papers against other papers published in the same year and source, or count just […]

There should be a metrics which weigh the author position. Typically first author does all the work. So the first authorship and the final authorship should have higher weightage compared to other authorships. Personally, I think the name appearing after the third author and before the final author should not have any weightage

[…] Many have attempted to fix the h-index weaknesses with various computational models that, for example, reward highly-cited papers, correct for career length, […]

[…] was re-reading a blog post by Alex Bateman on his affection for the H-index as a tool for evaluating up-and-coming scientists. He describes Jorge Hirsch’s H-index, its […]

1. Take out self citations 2. Take out review article citations 3. Have a negative field (topic) correction factor. 4. Have a negative correction factor for study section member, journal editor etc. 5. have a name and country correction factor.

Then let us compete…

I strongly agree about taking out citations for review articles. They totally distort the evaluation of a scientists’ worth. Reviews are cited much more highly than oritinal articles and contribute zero to the advancment of science by their authors. Less strongly, I also agree about self-citations because it is so hard to distinguish between genuine ones and irrelevant self-serving ones.

As both journal editor and study section member, I can assure you that neither capacity does anything to citations. I can’t think of anyone gratuitously citing my papers so that they can get preferential treatment. This is preposterous.

Finally, topic and country corrections are much more meaningful to apply to the final use of the h-factor than to its calculation. Whether that use is promotion, tenure, new appointment or funding, you are competing against your compatriot colleagues in the same field. Across countries, most responsible decision makers will apply a correction factor. When looking at post-doc applicants, I will rather take someone from, e.g. India with h=3 than from the US with h=5.

I do not quite agree with a country correction. Nobody cites you because you hail from a particular country. Your work is cited because its good, relevant and has helped somebody in their research and not just because you are a compatriot.

There is a bit more subtlety to country considerations. I agree that an author’s self-interest would strongly motivate them to cite the best paper that supports the point they are making, regardless of where the paper came from. However, we are judging the author here, not the paper. Across countries, there are huge discrepancies in terms of opportunities authors had to reach any given H value.

Put yourself in the position of a lab director going through post-doc applications. You narrow it down to two applicants, both H=5, one of them achieved it by working in the US, the other by working in, say, Tunisia. All other things being equal, who is more likely to have stellar performance in your lab?

This is what I mean when I say that the country correction should apply to the final use of the H, but not to its calculation.

If it comes down to selecting one among two-three people, the selection will fully be based upon how well the interview go with that person. Whether he will perform well or not will be gauged through the interview and not by the H index

[…] Source: blogs.plos.org […]

[…] but she send me a few links later on and while reading them through I slowly began to understand. This one she send me as well and helped me the most in understanding the whole […]

[…] A career in science depends more and more on quantitative measures that aim to evaluate the efficiency of work and the future potential of a researcher. Most of these measures depend on publishing output, thus many people conclude that we live in a “publish or perish” environment. Nowadays universities also face austerity measures and one could say, that we are living in “post-doc-apocalyptic” times, meaning that a large number of postdocs (working under temporary contracts) are competing for a small number of tenure positions. Altogether, universities make a very competitive environment. Quantitative indicators, like the h-index, are becoming more and more important in this competition. Should you take notice of them? If you want to work in academia, you should. You will probably see a lot of disadvantages in this output-orientated system, but this is where we are at today. Taking care of your career, might require strategic decision making, which has to take into account possibilities of improving your quantitative indicators also. What is the h-index? Despite the fact that its relatively new (it was described for the first time in 2005), the h-index has become an important measure of career development. Just today I saw an academic job offer with a minimum h-index value added to the list of requirements. The h-index is generally used for choosing candidates for promotions and grant fundings. It is very often used as an official criteria, but in other cases it can be used by referees or reviewers to evaluate research output, because it is easy for anyone to determine what is the exact value of this parameter (have a look here for a reasons to love h-index). […]

[…] h-index changes over time (though see Alex Bateman’s old blog post about “Why I love the h-index“, where he refers to the “h-trajectory”).  Does the “successful […]

[…] A. Why I love the h-index. PLoS […]

[…] on statistically assessing researchers based on publications, impact factors and H-index (Click for article on H-Index). Two people considered amongst the best scientists of the 20th century had this to […]

[…] https://blogs.plos.org/biologue/2012/10/19/why-i-love-the-h-index/ […]

h-index doesn’t make any sense. Although there are so many examples for that, few examples are given here. There is one scientist with h-index of 82 and 37,900 citations even at the beginning of her career. But she hasn’t written a single research paper in her life. Because she got the membership of many CERN groups in the world, she got her name among the thousand names given at the end of many research papers. She is not even a coauthor of any of those papers. Just because CERN members publish papers in so called US or European journals, google scholar search robot finds all those papers. Most of the CERN members got higher h-index and higher number of citations like that. On the other hand, there are some researchers who have written 70 to 100 papers. But they have a lower h-index below 10 and less number of citations, just because google search robot can’t find even many good journals. Google search robot easily finds US journals, because it thinks that US journals are reputed journals. When I was doing my Ph. D at early nineties, I read several research papers. I found one good paper with original data of ferrite thin films published by some Japanese scientists on a Japanese journal. Few years after that, I found that some US researchers have deposited the same material on the same substrate using same techniques. But the data of US researchers are worse than the data published by Japanese researchers. But US researchers have published their worse data even after one year in US journal of applied Physics. So how can someone say that US journals are the best?

[…] weaknesses with various computational models that, for example, reward highly-cited papers, correct for career length, rank authors’ papers against other papers published in the same year and source, or count just […]

[…] PLOS Biologue: Why I love the H-index – https://blogs.plos.org/biologue/2012/10/19/why-i-love-the-h-index/ […]

[…] Corregir según la duración de la carrera académica del autor […]

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Measuring academic impact: An author-level metric: the H-index

• Tracking your research impact: sources & metrics
• An author-level metric: the H-index
• Journal level metrics

An author-level metric: the h-index

The H-index, proposed by physicist J.E. Hirsch (hence the H) in 2005, is a way to measure the individual academic output of a researcher. "A scientist has index h if h of his or her Np papers have at least h citations each and the other (Np-h) papers have ≤h citations each" (Hirsch, 2005).

To determine the H-index of a researcher you need the list of the publications of that researcher and the number of citations each publication received. Then sort the publication list by the number of citations: the publication with the highest number of citations is number 1. The H-index is the number where the number of the publication in the list and the number of citations received are the same.

Example of an H-graph in Scopus (click on the picture to enlarge it)

The H-index has become a popular performance indicator, probably because the calculation of the h-index is easy to understand, and because it takes into account both productivity (the number of publications) and academic impact (the number of citations received). The H-index of an author is also easy to find: in Scopus, Web of Science and Google Scholar within a few mouseclicks (but be aware, that's the 'quick and dirty approach').

However, the H-index has various weak points. An easy example: the H-index doesn’t take into account the age of the researcher, which makes it unfair to use the H-index to compare two researchers in different stages of their research career. A professor at the end of his career has published more than a PhD-student and has had more time to get cited. There are also examples of researchers changing disciplines, taking their high H-index with them to a discipline with in general lower H-indexes. Because of differences in publication cultures between disciplines , you can’t compare the H-index of researchers from different disciplines.

Examples of researchers changing disciplines

From astrophysics to economics: Titus Galama . His Google Scholar Profile shows his H-index based on all his publications.

From psychology to media & communication: Jeroen Jansz . His Google Scholar Profile also shows his H-index based on all his publications.

Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102 (46), 16569-16572. https://doi.org/10.1073/pnas.0507655102

Which H-index?

To create the list of publications and the number of citations received you can use different sources. The most used sources are Scopus, Web of Science and Google Scholar. Which source you use, determines the H-index you find for a particular researcher, because the ‘publication universes’ of the sources are different. For example Google Scholar also indexes university repositories, so citations in student’s theses are counted as well.

The example below shows the general picture: the H-index based on publications in Web of Science is lower than the H-index based on publications in Scopus; the H-index in Google Scholar is by far the highest.

An example: Rutger Engels, Professor Developmental Psychopathology at ESSB and former rector magnificus of the Erasmus University Rotterdam. 

* In Web of Science we had to combine and clean several author records. In Scopus the Scopus Author Profile was used, in Google Scholar the Google Scholar Citation Profile was used. 

Our advice: When you are looking for your own H-index or the H-index of another researcher, don’t rely on easily-available numbers. The basis of the H-index is the publications list: you have to check whether the publications in the list you use are indeed of the researcher and whether publications might be missing. Always give information about the source used and use, when possible, multiple sources.

Determining the H-index in Web of Science, Scopus and Google Scholar

In these handouts you can find the steps to determine the H-index of a researcher in:

• The H-index in Google Scholar
• The H-index in Scopus
• The H-index in Web of Science
• YOU in databases: Academic profiling How can you make sure that your publications can be found easily in citation databases such as Scopus, Web of Science and Google Scholar? By managing your author profiles, such as Web of Science ResearcherID, ORCID iD, Google Scholar Citations profile or Scopus Author ID.
• EUR ORCID LibGuide Practical information on how to register for an ORCID iD, how to add publication data and other information to your ORCID record and how to use your ORCID iD in other systems.
• Research Evaluation and Assessment Service (REAS) A team of bibliometric practitioners of the University Library can give advice on how to evaluate academic impact and to create understanding of responsible metrics. Click on the header 'Contact the REAS team'.

Email the Information skills team

• << Previous: Tracking your research impact: sources & metrics
• Next: Journal level metrics >>
• Last Updated: Mar 13, 2024 11:08 AM
• URL: https://libguides.eur.nl/informationskillsimpact

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What Is a Good H-Index Required for an Academic Position?

Posted by Rene Tetzner | Sep 3, 2021 | Career Advice for Academics , How To Get Published | 0 |

What Is a Good H-Index Required for an Academic Position? Metrics are important. Even scholars who may not entirely agree with the ways in which academic and scientific impact is currently measured and used cannot deny that metrics play a significant role in determining who receives research grants, employment offers and desirable promotions. The h-index is only one among various kinds of metrics now applied to the research-based writing of professional scholars, but it is an increasingly significant one. Introduced by the physicist Jorge Hirsch in a paper published in 2005, the h-index was designed to assess the quantity and quality of a scientist’s contributions and predict his or her productivity and influence in the coming years. However, its use and importance have quickly expanded beyond physics and the sciences into a wide variety of disciplines and fields of study. If you are applying for a scientific or academic position, hoping for a promotion or in need of research funding, it will therefore be wise to give your h-index score some consideration, but within reason. In some fields, the h-index and other forms of metrics play a very small part if any in hiring and funding, and there are still many other means used by hiring and funding committees to assess scholarly contributions.

The h-index is considered preferable to metrics that measure only a researcher’s number of publications or the number of times those publications have been cited. This is because it combines the two, considering both publications and citations to arrive at a particular value. A scholar who has five publications that have been cited at least five times has an h-index of 5, whereas a scholar with ten publications that have been cited ten times has an h-index of 10. Publication and citation patterns differ markedly across disciplines and fields of study, and the expectations of hiring and funding bodies vary depending on the level and type of position and the kind and size of research project, so it is impossible to say exactly what might be considered an acceptable or competitive h-index in a given situation. H-index scores between 3 and 5 seem common for new assistant professors, scores between 8 and 12 fairly standard for promotion to the position of tenured associate professor, and scores between 15 and 20 about right for becoming a full professor. Be aware, however, that these are gross generalisations and actual figures vary enormously among disciplines and fields: there are, for instance, many full professors, deans and chancellors with very low h-index scores, and an exceptional young researcher with an h-index of 10 or 15 might conceivably still be working on a post doctorate.

As a general rule in many fields, an h-index that matches the number of years a scholar has been working in the field is a respectable score. Hirsch in fact suggested that the h-index be used in conjunction with a scholar’s active research time to arrive at what is known as Hirsch’s individual m. It is calculated by dividing a scientist’s h-index by the number of years that have passed since the first publication, with a score of 1 being very good indeed, 2 being outstanding and 3 truly exceptional. This means that if you have published at least one well-cited document each year since your first publication – a decent textual output by any measure – you are among a successful group of scholars, and if you have published two or three times that number of well-cited documents over the same period of time, you are among the intellectual superstars of your discipline and probably of your time. To put this into perspective, from what I can find online it looks like Stephen Hawking has a score of about 1.6 by this calculation. If you can approach a hiring committee or funding body with anything close to that, you are certainly going to be a serious contender in the competition.

The h-index as a measure of both the quantity and quality of scholarly achievement is considered quite reliable and robust, so it has proved incredibly popular and is now applied not only to individual researchers, but also to research groups and projects, to scholarly journals and publishers, to academic and scientific departments, to entire universities and even to entire countries. As with all metrics, however, the h-index is subject to a number of biases and limitations, so there are significant problems associated with relying solely on h-index scores when making important research and career decisions. The h-index does not, for example, account for publications with citation numbers far above a researcher’s h-index or distinguish any difference between publications with a single author or many. Older publications are counted exactly as more recent ones are and older scholars benefit, whether they have published anything new in years or not. Neither the length of a publication nor the nature of each citation (positive or negative) is considered, so those measures of quantity and quality are not part of the picture. Early career researchers who take the time to delve deeply into an important problem and eventually produce an excellent article and scholars at any stage in their careers who dedicate time to teaching or practical applications of research will have lower scores than those who crank out mediocre articles based on uninteresting research that is nonetheless cited by their colleagues. Finally, the databases from which the h-index and other metrics are determined vary in the types of documents they consider and the fields of study they include, so the same scholar will not receive the same h value across all of them, and accurate comparison across fields and disciplines is impossible.

These and other problems have generated a number of adjustments that are rather similar to Hirsch’s individual m, which, as discussed above, considers a scholar’s active research time in relation to his or her h-index. The g-index gives greater weight to publications whose citation counts exceed a researcher’s h value; the hi index corrects for the number of authors; the hc index corrects for the age of publications, with recent citations earning more counts; and the c-index considers collaboration distance between the author of a publication and the authors citing it. Solutions for comparison between disciplines and fields have included dividing the h-index scores of scholars by the h-index averages in their respective fields to arrive at results that can be compared, but defining fields can be tricky, and larger fields of study with more researchers naturally generate more citations. The databases used for scholarly metrics are constantly upgrading and broadening their inclusiveness to render metrics like the h-index more truly representative of a researcher’s actual productivity and impact, so the accuracy and consistency of these tools are likely to continue improving. However, no new numbers or calculations can add what all of these metrics lack, and that is research content – the valuable and unique content that makes the publication of research a worthy task in the first place.

Committees gathered to hire or promote faculty or to select the recipients of research grants rarely rely solely on metrics when making their decisions. If they are doing their jobs properly, they combine what they can gather from metrics with other information about candidates and their scholarly impact. They do not just notice how many times the papers of candidates have been cited; they read those papers and consider their content, and they pay attention to the other activities of the scholars they are considering. This wider perspective is appropriate for an applicant as well, so if you are polishing your CV, putting together a grant application or preparing for a job interview, look over your own unique achievements with a kindly yet critical eye and consider them in direct relation to what the job posting or grant regulations indicate is wanted. If you happen to have a wonderful h-index score or any other impressive metrics, by all means flaunt them, and if you fear that a low h value will compromise your career aspirations, do what you can to have your publications with lower citation counts read and used more often, update your profiles on the relevant databases, and publish the type of document sure to garner citations in your field, such as a review article.

Do keep in mind, however, that hiring and funding committees are often looking for far more than large numbers of highly cited publications. Admittedly, they rarely balk at them, but universities are also seeking excellent teachers, advisors and administrators, so play up those skills and any related experience you have, and remember that financial supporters of research may be keen to fund scholars who can successfully manage and complete projects, even and perhaps especially if part of the training they offer younger researchers means that their students tend to publish most of the results. Finally, an active online presence in your field established through sharing your research via blogs, professional platforms and social media might not garner the same respect as formal publications, but it can count for a great deal when many universities are working to increase their online activities and funding bodies working to democratise the publication of the research they support. Generally speaking, committees considering applications will be even more likely to google the names of candidates and applicants than to look up the metrics associated with them, so assume that both will be done and ensure that what can be found shares excellent research content and leaves a desirable professional impression of you and your work.

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What Is a Good H-Index Required for an Academic Position? The h-index is used along with applicants research & skills to measure their impact

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Predicting your future h-index

Study aims to forecast success.

Teaming up with colleagues from diverse academic backgrounds is a key factor in building a researcher's h-index. Credit: agnormark/Getty Images

23 September 2020

agnormark/Getty Images

Teaming up with colleagues from diverse academic backgrounds is a key factor in building a researcher's h-index.

Collaborating with leading researchers and having co-authors from diverse academic backgrounds are key factors in building an individual’s h-index , a metric used to gauge a researcher’s academic success, a new study has found.

Analyzing a researcher’s scientific impact through the lens of their h-index is common practice for assessing funding applications and identifying mentors and collaborators. The study authors set out to find which factors most influence a scientist’s future scholarly achievements, judged by their h-index over five, eight and ten years.

The h-index metric is widely used to measure a researcher’s productivity and impact, based on their article output and citations over time. It was invented in 2005 by University of California physicist Jorge Hirsch, as an alternative to raw citation counts , to recognize the cumulative achievements of authors with good citation levels over a range of papers, rather than “one-hit wonders.”

Including highly-cited references, publishing in journals with a high impact factor, and joining a renowned institution were some of the other factors identified in the study that influenced an author’s future h-index.

The six study authors, all of them data scientists based in China, the United States and Australia, used big data and machine learning techniques to analyze two data sets; one related to nearly 80,000 computer scientists and the 105,000 articles they published, and the other of just over 80,000 physicists with 98,000 articles.

“We tried to cover as many as possible causal factors to predict a scholar’s scientific success measured in his/her future h-index,” says the lead author Kong Xiangjie, a computer scientist at Zhejiang University of Technology in Hangzhou, China.

Predicting future h-index

The study , which was published in May 2020 in ACM (Association for Computing Machinery) Transactions on Knowledge Discovery from Data (TKDD), identified 35 causal factors in five broad categories associated with a scientist's future h-index:

author-centered, based on factors such as the h-index of a scientist’s co-authors, the maximum h-index value of his/her co-authors and the differences between the maximum and the lowest h-index value of their co-authors;

article-centered, with factors covering a scientist’s citation counts and publication numbers, the frequency with which their topic is covered in the literature, and the highest and the lowest citation numbers of the references in their publications;

venue-centered, referring to factors concerning the journals in which a scientist publishes;

institution-centered, looking at a scientist’s colleagues in terms of their h-index, their number of publications and their citation counts, as well as the distribution of these indicators among all scientists at the same institution; and

temporal factors, including a scientist's academic age and the change in their h-index over time.

The study found author-centered and article-centered factors had the strongest bearing on a scholar’s future h-index. To confirm their empirical results, the study authors used machine learning to model a scholar’s potential evolution over time. From given starting conditions, variations were introduced with the addition or removal of conditions.

The aim was to compare predictive results with actual values in scholars’ careers and to verify causal relationships.

Kong Xiangjie et. al

According to the study , article-centered factors in the computer scientists dataset have 41.47% importance, author-centered factors have 25% importance, the temporal-centered factors have 16.67% importance, and the venue and institution-centered factors have 8.33% importance.

In the physicists dataset, article-centered factors have 33.42% importance, author-centered factors 42.33% temporal-centered factors 6.39% importance, venue-centered 8.5% importance, and institution-centered factors have 9.36% importance.

They also found that the h-indeces of scholars in the same institutions tend to be very close to each other.

Success factor warning

Liu Yuxian, a librarian at Tongji University, Shanghai, welcomed the study, but questioned the characterization of some factors as both causing success and indicative of it.

For example, an author's citation and publication numbers, identified as article-centered causal factors, are also components of the h-index used as the indicator of scientific success. She also warned against any use of the h-index in isolation to gauge success.

Zhao Rongying, an informatics researcher in the Research Center for Chinese Science Evaluation at Wuhan University, praised the study's originality, but questioned the role assigned to venue-centred (journal) factors in as contributors to an individual’s future success.

Kong acknowledged the dual use of article and author elements for both cause and effect, but argued that the machine learning technique confirmed the early role of citation and publication numbers in a scholar’s eventual h-index success, with other factors coming into play along the way.

"As for h-index, we know its limitations (in measuring one's academic success), but currently, there is no ideal, widely-accepted alternative," he told Nature Index.

Hao Yufeng, a physicist at Nanjing University, said that some factors the TKDD study revealed are instrumental in guiding young scientists' career development.

"Collaborating with both top scientists and junior ones such as doctoral students, pursuing hot topics, and co-authoring with others with multidisciplinary backgrounds, these are very reasonable strategies for future success, particularly to those young PIs (primary investigators).”

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What is a good h-index?

This article discusses what’s considered a good h-index. If you’re interested in a tool that helps you when improving your h-index, check out Avidnote which is a useful app to help you write better research notes.

It is not an easy task to quantitatively determine what makes one scholar more prolific than another, or compare between the bodies of work of different researchers. Although comparisons between researchers is difficult, it is nonetheless important when allocating research grants or deciding on who to recruit or to promote to an academic position. One way to determine the level of output of a researcher is by using the h-index. Since it was introduced many years ago, it has become widely used because of its unique properties.

What is the h-index? The h-index is a measure of a researcher’s productivity (number of publications) and impact (number of citations generated by the publications). It takes into account both the quantity of work, in terms of how many publications the author has generated, as well as the “ quality ” of those papers, in terms of how many citations it has obtained (assuming that accumulating a lot of citations is the mark of quality, which is not necessarily true, but in lack of other metrics, it’s used as a proxy to measure quality).

The index was introduced in 2005 by Jorge Hirsh, a physicist at the University of San Diego, California in a paper titled “An index to quantify an individual’s scientific research output.” Hirsh defined the index as “the number of papers with citation number ≥h [the ranking position where the number of publications is greater than or equal to the number of citations]” and saw it as a “useful index to characterize the scientific output of a researcher.”

According to Hirsh

“The publication record of an individual and the citation record clearly are data that contain useful information. That information includes the number (Np) of papers published over n years, the number of citations (Nc j ) for each paper (j), the journals where the papers were published, their impact parameter, etc. This large amount of information will be evaluated with different criteria by different people. Here, I would like to propose a single number, the ‘‘h index,’’ as a particularly simple and useful way to characterize the scientific output of a researcher. A scientist has index h if h of his or her Np papers have at least h citations each and the other (Np – h) papers have ≤h citations each.”

He further notes that

“For the few scientists who earn a Nobel prize, the impact and relevance of their research is unquestionable. Among the rest of us, how does one quantify the cumulative impact and relevance of an individual’s scientific research output? In a world of limited resources, such quantification (even if potentially distasteful) is often needed for evaluation and comparison purposes (e.g., for university faculty recruitment and advancement, award of grants, etc.).”

Practical example

Consider a scientist that has published 5 papers with the following citations:

• Publication 1 = 11 citations • Publication 2 = 9 citations • Publication 3 = 7 citations • Publication 4 = 4 citations • Publication 5 = 3 citations

In the above example, the scientist’s h index is = 4 [the ranking position where the number of publications is equal to the number of citations]. Hence, paper 5 [or the fifth position in the ranking] is not relevant since the number of citations [3] is not greater than or equal to h[5]. Note that the citations are always arranged in descending numerical order. Thus, a scholar with 9 publications that have been cited at least 9 times each has an h-index of 9.

Because of its simplicity and logic, the h-index has become increasingly popular over the years. It is now a quick and accessible metric that researchers can use to track their scientific progress or impact. Different databases (e.g., Google Scholar, Scopus, Web of Science, etc) have specific ways of calculating the h-index. Determining a good h-index

Due to some other variables, it is not possible to precisely determine what constitutes a standard or uniform good index. This is because publication and citation dynamics vary across the various fields of learning. There are also similar variations among funding and recruitment organizations that may consider factors like educational level, position of the applicant as well as type and size of the research project to be funded.

So what is a “good” h-index?

Here, it could be useful to refer to a study (Am J Clin Pathol 2019;151:286-91) that found that, on average, assistant professors have an h-index of 2-5, associate professors 6-10, and full professors 12-24. The statistical variance in the data set was quite large so the averages should be taken with a grain of salt. The same study found that if you are aiming for a Nobel Prize, your h-index needs to be at least 35 and preferably much closer to 70.

Note that the above figures are not fixed and conditions can differ significantly according to discipline (as noted above). For instance, there are cases where full professors, faculty deans, and vice-chancellors have very low h-index scores, while brilliant young researchers still pursuing PhDs have scores of between 10 and 15. The figures will also vary significantly depending on the research area.

One rule that is widely accepted, however, is that an h-index score should at least be equal to the number of years a scholar has put into his or her work. This rule was prescribed by Hirsch who recommended an h-index of at least 20 after working for the same number of years. One notable observation from Hirsch’s paper is that accomplished scientists usually accumulate high h-index scores. This is indicated by the statistics in his 2005 paper which show that 84% of Nobel laureates in physics had an index score of at least 30.

Limitations of the h-index

One significant pro of the h-index is its combination of productivity (number of papers published) and impact (number of citations) to derive the score. The implication of this is that neither several publications with a few (or no) citations nor a few heavily cited publications will lead to a high h-index. However, despite its strengths, the index has a number of weaknesses or limitations. Some of them are enumerated below.

• Due to incomplete coverage or varying degrees of coverage of a scholar’s works by the different bibliometric databases, the scholar’s h-index can vary significantly from one database to another. For example, some databases do not provide sufficient coverage for publications in foreign languages. • It fails to consider the number of authors of a particular publication. The argument here is that a paper with 70 citations written by one author should attract extra plaudits than one with the same number of citations written by say, 7 authors. • Because of its direct relationship with time, the index tends to favour writers who are at the middle or terminal stages of their working life. For instance, assuming Albert Einstein died in early 1906, he would still have been regarded as a very influential physicist but his h-index would have been lower (maybe 4 or 5). He presently has an h-index of 119, according to Google scholar. • Authors working in fields characterized by fast rates of publication, heavily cited/referenced papers, and multiple authorship tend to be more favoured.

• Review articles often exert a more significant impact on the h-index than original papers because they are usually cited more frequently.

Due to the limitations of the h-index, some variations have emerged over the years. These include m-index, contemporary h-index, and i10 index. However, these other measures are not as widely applied as Hirsch’s index.

Even with its limitations and skepticism amongst some scholars, metrics like the h-index remain important considerations among decision-makers that determine things like awarding research grants and recruiting staff. The h-index has continued to expand in usage since it was introduced by Jorge Hirsch in 2005. However, it is impossible to determine what constitutes a good h-index due to a number of factors such as differing publication and citation conditions across the various fields of knowledge, among others.

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Have You Calculated Your H-Index?

As an academic researcher, the quality of your research publications has traditionally been measured by the prestige of the journals in which your work has been published. The prestige or impact of those journals is usually measured by tracking the average number of citations that articles published in those journals receive over a two-year period.

The impact factor , or rating of that journal thus has a commonly accepted metric to substantiate a broader industry acknowledgment that the journal qualifies as being “prestigious.”

Indicates Personal Contribution in a Research

For a researcher looking to substantiate the quality or impact of his or her individual research output, a different metric is called for. The h-index , created by J.E. Hirsch, a physicist at the University of California, in 2005, was first proposed as a measure of scientific output of any research entity, including research institutions and even entire countries. But it has found broader acceptance as a measure of individual research contribution.

The h-index measures both the number of papers you have published and the number of citations those papers have received. As such, it discounts any publications in less-prestigious journals with the inherent assumption that they will receive fewer citations.

Simple Math

The calculation of the h-index requires an accurate count of the number of articles published and the number of citations received. Access to a subscription-based service such as Scopus makes the calculation much easier, but there are other freely available tools out there.

As an example, if a researcher has an h-index of 7, it means that he or she can lay claim to 7 or more published papers, and that at least 7 of those papers have been cited 7 times.

The higher the number, the larger the number of papers and citations. The results are comparable within academic disciplines, but since some fields publish more frequently than others, the data is not comparable across disciplines.

Overcoming Limitations

Since the index is based on the volume of papers and citations, it skews in favor of researchers with academic careers of a decade or more. If you’re just starting out in your academic career and you’re presenting your work to a grant committee for research funding, a low index score won’t count against you, but it won’t look impressive against a competing researcher with a longer track record.

In that instance, you might be better served to present a combination of the journal impact factor score as well as your h-index score to at least show that what papers you have published were at least published in (hopefully) prestigious journals.

In 2013, Nature published a paper from three Chicago-based researchers who proposed an algorithm that would help new researchers with relatively short publication track records. By examining the total number of publications, the length of time since the first publication, the number of different journals, and the number of top-ranking journals, the proposed algorithm would predict the researcher’s h-index five years into the future. It remains to be seen as to whether grant or tenure committees will give any credence to such predictions.

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Comparison of scientometric achievements at PhD and scientific output ten years later for 4,790 academic researchers

Gyöngyi munkácsy.

1 Department of Bioinformatics and 2nd Dept. of Pediatrics, Semmelweis University, Budapest, Hungary

2 TTK Lendület Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary

Péter Herman

Balázs győrffy, associated data.

All relevant data are within the paper and its Supporting Information files.

Introduction

PhD is the highest awarded degree offered by universities in different disciplines. Owners of a PhD can teach at universities, start independent research and receive a higher salary while further building a scientific career. We examined whether the publication output before the PhD degree has a correlation with subsequent research activities.

We downloaded publication and citation data from the Hungarian Scientific Bibliography for Hungarian researchers who obtained PhD between the ages of 24 and 45. The researchers were grouped into eleven scientific sections. We examined the number of Q1 publications published in the previous 5 years, the H-index, the total number of citations for the last complete year, and the biological age of the researcher. Each parameter was computed for the year at which the PhD was obtained and ten years later. Pre-PhD publications (and citations for these) were excluded when assessing post-PhD track records. Spearman rank correlation and Kruskal-Wallis test were computed.

We analyzed all together 4,790 researchers. We obtained a positive correlation between the number of Q1 publications before and after PhD (corr. coeff. = 0.21–0.54, p<0.01 in all sections), between the H-index before and after PhD (corr. coeff. = 0.32–0.56, p<0.01 in all sections), and between the citations received before and after PhD (corr. coeff. = 0.34–0.51, p<0.01 in all sections). All three metrics measured ten years after the PhD were negatively correlated with the age of the researcher at the time of obtaining the PhD (number of publications corr. coeff. = -0.09–0.22, p<0.05; H-index corr. coeff. = -0.09–0.29, p<0.08; number of citations corr. coeff. = -0.14–0.30, p<0.01). Among all disciplines, Philosophy and History and Engineering sciences show the strongest correlation between pre- and post-PhD output. When running multiple regression analysis for all three metrics as dependent variables and the number of articles, the H-index, the number of citations in the year of the PhD, the calendar year of PhD, and the gender of the researcher as independent variables, the number of articles and the H-index in the year of PhD reached the strongest positive correlations while gender had a negative correlation.

Conclusions

We independently evaluated pre- and post-PhD publication performance. In connection with age, the discipline-specific reference values of scientometric parameters at the time of obtaining the PhD can help to select candidates for postdoctoral grants and positions.

Doctor Philosophiae (PhD) is the highest globally recognized academic qualification available in any field of research. A PhD degree can only be awarded by a university once the applicant has completed a wide-ranging and novel research project. The holder of a PhD degree can teach at universities, start independent scientific research, and also receives a higher salary as better paid positions are reserved to those with a PhD degree. For example, in Hungary only the two most basic academic (assistant lecturer) and scientific (assistant research fellow) positions can be filled by those without a PhD degree. Notably, the effect of education on wages was also sizeable in an Italian study [ 1 ], and over-education even had a negative impact [ 2 ]. The number of PhD graduates grows rapidly in all continents of the world–in some countries like China by 5% a year [ 3 ].

Commonly, the PhD student is assisted by a supervisor and the training requires 3–6 years of full-time investment. As cornerstones, completing the studying requirements, passing the doctoral examination, and the successful defense of the PhD dissertation is needed to obtain the degree. The so-called doctoral dissertation proves that the researcher can independently solve a scientific task, with which it broadens our knowledge with new, previously undiscovered results in the field [ 4 ]. The significance of the work made by PhD fellows is supported by the fact that about one third of research publications comes from a doctoral student in universities [ 5 ].

The exact conditions for obtaining the degree vary from country to country. In the UK, a PhD is awarded as part of a three-year course including specific and general subjects. Students’ dissertations are reviewed by external reviewers and an oral defense is delivered to show the candidates’ proficiency. In Australia, training with a scholarship lasts for 3–4 years [ 6 ] and publications accepted before the application can also form the basis of the doctoral thesis in certain disciplines [ 7 ]. Oral presentation for defending the dissertation is not mandatory. The United States has the longest PhD program with 4–11 years for graduates depending on subject areas. In Germany, most doctoral candidates work as employees in universities and training decisions are left to supervisors and doctoral students [ 8 ]. In Hungary, a two-year study period ending with a final exam is extended by a three-year research period, and the defense of the doctoral dissertation finishes the training.

A number of indicators are available to measure the quality and quantity of scientific output. Available bibliometric indicators include among others the total number of publications, the cumulative impact factor of all publications, the total number of citations, the number of articles with at least one citation, the number of highly cited articles, the average number of citations per article, the number of citations per year, and the H-index [ 9 – 11 ]. Derived bibliometric indicators have also been developed to measure researcher productivity [ 12 ]. There are no international standards for the publication requirements for obtaining a PhD [ 13 ]. The minimal requirements are mostly determined by the program, the lead tutors, and by the university. In Australia, 2–5 first or co-authored articles are required to get PhD degree [ 7 ]. There is no regulation in the United States and in Canada, although publication is strongly recommended before obtaining a PhD [ 13 ]. Peer-reviewed publication is not required in Germany. In Hungary, a discipline-specific number of publications with or without an impact factor threshold are needed to apply for a degree [ 14 ].

The scientific outcome of a doctoral training is determined by several factors in addition to the personal abilities of the doctoral student: the doctoral program, relevance, novelty, working environment, access to other experts, feasibility, and supervision [ 15 ]. The theoretical conditions of a good doctoral dissertation are also established [ 16 ]. Generally, a PhD is said to be strong in case the number of publications of the candidate is high, the total impact factor of these articles is high, and the doctoral student fulfills the training conditions in a shorter time.

To what extent does PhD training influence postdoctoral publication performance? Here, we aimed to correlate independently calculated pre- and post- PhD scientific output for a large cohort of Hungarian researchers spanning all scientific disciplines. In addition to different publication metrics we also aimed to include the age of the doctoral student to determine which features have the highest influence on a researcher’s subsequent career.

Database construction

Publication and citation data were downloaded from the Hungarian Scientific Bibliography ( www.mtmt.hu ). MTMT includes self-reported data, which is then validated at the time when one submits his or her PhD thesis. In this, we included doctors of the Hungarian Academy of Sciences (HAS), members of HAS, recipients of the Momentum grant (an ERC-grant like national scheme), researchers who have submitted Hungarian Scientific Research Fund (OTKA) applications since 2006, and researchers with university affiliation who have obtained a PhD. Unique MTMT identification numbers were used to distinguish researchers with the same name. The age of researchers as well as the age at obtaining the PhD degree was obtained from the doktori.hu public database.

Scientific sections

HAS classifies researchers into eleven scientific sections, which are as follows: I. Language and Literature, II. Philosophy and History, III. Mathematics, IV. Agriculture, V. Medicine, VI. Engineering, VII. Chemistry, VIII. Biology, IX. Economics and Law, X. Earth sciences, and XI. Physics. Researchers can select the most relevant section based on their area of research. For researchers without a selected scientific section the designation was made based on the topics of the last five publications.

Indicators of scientific output

We computed three indicators to measure scientific performance, the selection of these was based on our previous study [ 11 ] and availability (e.g. we had to exclude journal impact factors as these are not available for all publications). These include the number of scientific publications , the H-index , and the number of citations for all previous publications in the given calendar year.

The number of all scientific publications is based on the total number of articles published in Q1-ranked journals in the last five years as a first, last, or corresponding author. By excluding Q2, Q3, and Q4-rated articles we guaranteed that only high-quality publications are included in the database. This number reflects the contemporary scientific activity of the researcher. Q-ranking was based on the Scimago Journal Rank database ( https://www.scimagojr.com/ ).

The H-index of a researcher is n if he/she has published n articles, each of which has been cited at least n times while there are no other articles with more than n citations. The number of citations includes both dependent and independent citations. This value is an indicator of an individual’s performance over his/her entire academic career and is a measure independent of the impact of journals. We have computed two different H-index values: the H-index at PhD includes all publications up to the year of the PhD award. The H-index ten years after PhD includes only publications published after the PhD award . The aim of this differentiation was to exclude the direct effects of publications before the PhD on subsequent H-index values ( Fig 1 ).

The total number of independent citations to all previously accepted scientific articles in a given calendar year. Independent means that there is no overlap in the author list of the cited and the citing documents. This value is an indicator of the impact of the researcher’s former scientific activity in the present. By excluding dependent citations, it can be guaranteed that this parameter will give an objective evaluation of the researcher impact. In the same way as for the H-index, to exclude the effect of pre-PhD papers, the independent citation count was derived by using only citation received for papers published after the year of the PhD.

Statistical analysis

The age of the researcher and all three parameters achieved in the year of the PhD (the number of articles published in the previous 5 years, H-index and number of independent citations) were compared with those values obtained ten years after the PhD. Continuous variables were compared by calculating Spearman correlation coefficients. Differences between sections were calculated using Kruskall-Wallis test. The p-value cutoff was set at p = 0.05.

The initial database contained 7,118 researchers. Those researchers were deleted where the year of obtaining the PhD was unknown, who obtained the PhD over the age of 45 or under the age of 24. We also excluded those who obtained their degree within 10 years because we could not perform the analysis with these data. Degrees obtained over the age of 45 were excluded as these more likely refer to a former candidate degree and not to a PhD degree. Values under the age of 24 were most likely date errors in the database. The final database contains 4,790 researchers. The screening process is summarized in Fig 2 .

The 4,790 researchers were arranged to one of eleven scientific sections of HAS, in particular, 342 researchers to Language and Literature, 457 researchers to Philosophy and History, 238 to Mathematics, 429 to Agriculture, 702 to Medicine, 360 to Engineering, 467 to Chemistry, 719 to Biology, 489 to Economics and Law, 242 to Earth sciences, and 345 to Physics. The average age of all researchers was 59.8 years, with a median of 58 years.

Features of researchers in the year of obtaining their PhD per section

The average age of researchers at the year of obtaining the PhD degree was 33.8 years. There was a significant difference between scientific sections in this parameter (Kruskall-Wallis p value<1E-16). Youngest mean age at obtaining the PhD was shown in Mathematics, Physics and Biology sections (31.4, 31.7 and 32.8 years, respectively). Researchers in Language and Literature, Medicine, Economics and Law sections had the oldest average age (35.3, 35.1 and 35.1 years, respectively) (see Fig 3A and all the values in S1 Table ).

Average age at obtaining the PhD degree (A), number of publications in the previous five years (B), H-index (C), and yearly independent citations (D) at the year of obtaining the PhD in each scientific section. Mean and 95% CI are shown. See detailed data in S1 Table .

There was a significant difference between sections in the average number of peer reviewed publications in the five years preceding the PhD (Kruskall-Wallis p value<1E-16) ( Fig 3B and all the values in S1 Table ). The average number of publications varied between 0.01 and 1.0. Researchers in Language and Literature, Economics and Law, and Philosophy and History sections had the lowest average values (0.01, 0.05 and 0.1, respectively). The highest average number of publications was found in Physics, Medicine and in Biology sections (1.00, 0.80 and 0.78, respectively). The average number of publications when including all researchers was 0.48.

Mean H-index at the year of obtaining the PhD degree in each section is presented in Fig 3C . Average values varied between 0.87 and 3.72. Researchers in Language and Literature, Economics and Law, Philosophy and History sections had the lowest average H-index values (0.87, 0.96 and 1.20 respectively). Highest average H-indices were found for researchers in Physics, Medicine and in Biology sections (3.72, 3.32 and 3.31, respectively). Average H-index at the year of obtaining the degree was 2.23 for all researchers.

Finally, the number of independent citations in the year of PhD varied between 0.83 and 13.22, and differed significantly between sections (Kruskall-Wallis p value<1E-16) ( Fig 3D and all the values in S1 Table ). Lowest average yearly citations were found for researchers in Language and Literature, Economics and Law and Engineering sections (0.83, 1.37 and 1.74, respectively). Researchers in Physics, Biology and Medicine sections showed the highest values for the average number of yearly citations (13.22, 11.86 and 11.56, respectively).

Total number of articles between 5–10 years after obtaining the PhD

Correlation between the number of articles accepted before obtaining the PhD and the number of articles accepted between 5–10 years after the PhD was the strongest in Mathematics, Philosophy and History, and Engineering sections (Spearman corr. coeff. = 0.54, 0.48, and 0.44, respectively, p<0.01). Weakest correlation was found in Language and Literature, Medicine, and Economics and Law sections (corr. coeff. = 0.21, 0.23, and 0.26, respectively, p<0.01). Positive correlation was found between all three scientific parameters in the year of PhD and this parameter, in each section. Table 1 shows Spearman correlation coefficients between the number of manuscripts 5–10 years after the PhD and the scientific parameters at the year of obtaining the PhD per section.

Correlation between the number of publications ten years after obtaining the PhD and scientometric parameters and age in the year of the PhD.

H-index at ten years after obtaining the PhD

Positive correlation was found between the H-index at the year of obtaining PhD and at ten years later in all sections. H-index of researchers in Philosophy and History, Engineering, and Mathematics—Agriculture sections in a tie showed the strongest correlation (Spearman corr. coeff. = 0.56, 0.52, 0.51, and 0.51, respectively, p<0.01). Weakest correlation was found in Biology, Medicine and Physics sections (corr. coeff. = 0.32, 0.39 and 0.40, respectively, p<0.01). We found positive correlation between the number of accepted manuscripts prior to PhD and value of H-index at 10 years after PhD– except in Language and Literature section (corr. coeff. = 0.05, p = 0.18). The number of independent citations in the year of PhD and value of H-index ten years after PhD had strong positive correlation in all sections. Table 2 shows Spearman correlation coefficients of the H-index values ten years after PhD and the other scientific parameters at the year of obtaining the PhD per section.

Correlation between H-index ten years after obtaining the PhD degree and scientometric parameters and age in the year of the PhD.

The number of independent citations in the tenth year after obtaining the PhD

Positive correlation was found between the yearly number of independent citations in the year of obtaining the PhD degree and ten years later in all sections. Researchers in Philosophy and History, Mathematics and Engineering sections showed the strongest correlation (Spearman corr. coeff. = 0.51, 0.49, and 0.47, respectively, p<0.01). Lowest correlation was found in Biology, Physics, and Language and Literature sections (corr. coeff. = 0.34, 0.36, and 0.38, respectively, p<0.01). We found positive correlation in all sections except of Language and Literature between the number of publication prior to PhD and the yearly number of independent citations ten years after PhD . Also, positive correlation was found between the H-index at the year of PhD and the number of independent citations in the tenth year after PhD in all sections. Table 3 shows Spearman correlation coefficients for the number of independent citations in the tenth year after PhD and the other scientific parameters at the year of obtaining the PhD per section.

Correlation between the number of citations in the tenth year after obtaining the PhD and scientometric parameters and age in the year of the PhD

The age of researchers at the year of obtaining the PhD and later scientific output

We found negative correlation between the age of researchers at the time of PhD and the number of publications at 5–10 years after PhD in all sections ( Table 1 ). Researchers in Biology, Earth sciences and Engineering sections showed the strongest correlations (Spearman corr. coeff. = -0.23, -0.22, and -0.21, respectively, p<0.01). Weakest correlation was found in Economics and Law (corr. coeff. = -0.09, p = 0.02), Agriculture (corr. coeff. = -0.10, p = 0.02), Physics (corr. coeff. = -0.13, p = 0.01), and Philosophy and History sections (corr. coeff. = -0.13, p<0.01). We found negative correlation between the researcher’s age at the time of obtaining the PhD and H-index ten years after the PhD in all sections–the significance was only marginal in Mathematics ( Table 2 ). Negative correlation was found between the age of researcher at the time of obtaining the PhD and the number of independent citations in the tenth year after PhD in all sections ( Table 3 ).

Gender specific differences

We were able to determine the gender for all researchers and compared the number of publications, the H-index, and the yearly independent citation count values reached by male (n = 3,689) and female (n = 1,101) researchers. At the time of obtaining the PhD, female students had higher mean publication count (Mann-Whitney p = 1E-07). There were no significant differences in the mean citation count and in the H-index values. Similarly, there were no significant differences between male and female researchers ten years after PhD in the three investigated scientometric parameters.

Multiple regression

In a separate analysis we performed multiple regression by simultaneously including the number of articles, the H-index, the number of citations in the year of the PhD, as well as age, the calendar year of PhD, and the gender of the researcher for all included scientists. Dependent variables were the number of articles, the H-index, and the number of citations ten years after obtaining the PhD. In this analysis, citation in the year of PhD had no significant correlation with the number of articles (p = 0.15) and the H-index (p = 0.63). Female gender was associated with lower H-index (p<1E-50), citation count (p = 1.1E-06), and number of articles (p = 1.2E-13) ten years after PhD. The most significant positive correlations for all three dependent variables were observed for the number of articles (p = 4.4E-32 for number of articles ten years after PhD, p = 2.4E-48 for the H-index ten years after PhD, and p = 9.4E-42 for the citation count ten years after PhD) and for H-index in the year of PhD (p = 9.2E-32 for number of articles ten years after PhD, p = 1E-50 for the H-index ten years after PhD, and p = 2.6E-13 for the citation count ten years after PhD). Age and the year of PhD had minimal effects with small correlation coefficients in most settings. The detailed results for each setting including the equation values are provided in S2 Table .

Publishing during PhD training has an impact on later careers, reputation, and collaborations [ 17 ]. Here, we partially reproduced the results of Horta and Santos, but by accounting for different scientific disciplines and assessing the effects for each disciplinary area. Thus, our results not only build on and validate the previous results [ 17 ] but also significantly extend the knowledge in this field.

Early career publications are seen as a requirement to enter an academic career [ 18 ]. Along with the excellent educational and professional activity, writing strong articles is an essential condition for promotion in a scientific career [ 19 ]. However, the question remains whether scientific publication output during the PhD adequately reflects future academic performance independently of the research environment [ 6 ]? Although Horta and Santos partially responded to this question, here we performed a more in-depth analysis including different scientific disciplines. We scrutinized Hungarian researchers whether the scientific performance before the PhD influences postdoctoral scientific output. We examined three bibliometric parameters in the year of obtaining the PhD and ten years later: the number of first, last- or corresponding authored manuscripts accepted in Q1-ranked journals, the H-index, and the total number of independent citations received by the researcher in the given year. In almost all settings each of the metrics measured at the time of obtaining the PhD was positively correlated with the values measured 10 years after the PhD award. These observations are in line with our previous study analyzing publication performance of Momentum grant holders before and after grant award [ 9 ] and reinforces the validity of the Matthew effect in this setting as well.

When looking on different scientific field-specific variances, we observed the weakest correlations in Medicine, Biology, and Physics sections for all three parameters compared to other sections. Most probably, the daily routine work of physicians in patient treatment often discourages publication activity [ 20 ]. On the other hand, bibliometric indicators achieved at the year of obtaining PhD are outstanding in Medicine, as confirmed by previous other studies as well [ 21 ]. Of all scientific sections, Mathematics, Engineering, and Philosophy and History had the highest correlations between pre- and post-PhD scientific output for all three investigated indicators. These observations can be partly explained by the fact that the highest salaries for PhD holders are available in engineering, business and science fields in the United States and in Hungary which can increase compliance and engagement during PhD studies.

We obtained a strong negative correlation between age at obtaining a PhD and subsequent scientific output in all disciplines. Younger age at PhD results in significantly better postdoctoral publications with higher impact. Our results are little bit astonishing, as we have evaluated pre-PhD and postdoctoral publications completely independently. Thus, the scientometric parameters ten years after PhD only reflect the H-index, citation, and publication count of publications printed after finishing the PhD studies. Because of this separation, those who obtain a PhD at a younger age had no advantage in terms of additional years to collect citations to increase their H-index over those who acquired their PhD later.

Publication metrics are not the only indicators of the performance of an individual, but also of an institution [ 22 ], and can be used to pave the way for access to external funding sources. Therefore, a main goal of universities worldwide is to maximize research output. Different tactics can be executed to intensify publication activity: writing courses, writing support groups, and writing coaches can significantly increase the number of publications of the research participants according to a study summarizing 17 studies [ 23 ]. Reviews also help to grasp available methods for scientific writing [ 24 ]. Other options include the continuous mentoring even after the competition of the PhD [ 25 ]. Overall, the requirement to publish is a constant high pressure for employees working in academic institutions [ 26 ].

Here, we did not examine personal factors such as periods of motherhood, in which case publication activity may be paused for years. The reason for this is the lack of available data regarding maternity leaves for PhD students. Although executed in a different country, a study involving 8,544 researchers from the United States found no effect of personal circumstances including marriage, number of children, and care for aging parents on publication productivity [ 27 ]. More important factors seemed to be academic rank, salary, commitment to research, and desire for recognition, which we could not examine due to the lack of available data. The availability of funding resources can also influence publication efficiency. Reputation and experience of the supervisor [ 6 ] or of the group leader [ 28 ] also impacts the productivity of a researcher. The country-specificity of these effects should be evaluated in a future study.

We have to note a few limitations of our study. In the analysis we did not have any explanatory variable for the PhD training itself. In other words, one does not know how the PhD was trained by the supervisors, if there was co-supervision or not, if one was included in a large or small research group, if one had access to resources to do research, etc. Second, one may publish during the PhD even if the training was not focused on publication. Sometimes it all depends on the earlier training of the students, and the ability of the students. In addition, we included only Hungarian researchers. The reason for this is the utilization of the MTMT database, which only comprises data for Hungarian researchers. A main advantage of MTMT is the differentiation between dependent and independent citations. Other repositories (like Google Scholar or Scopus) either do not have this information or there is no open access. Finally, we included only Q1 articles as our focus was on research excellence and not on research quantity. One might note that lower ranked publications might still have value as scientific results. We have to note however that the majority of all publications of the investigated researchers were published in a Q1 ranked journal.

In the future, a similar study with additional data can bring more light into this field. For example, the association between time to degree, available funding, and accomplished publications during the PhD may be relevant in order to understand how funding can influence the ability to publish or not during the PhD. Conversely, funding during the PhD can affect the research performance later on.

In summary, a major novelty of our analysis is the independent analysis of pre-PhD and post-PhD scientometric parameters. Therefore, in our analysis pre-PhD publications do not directly influence the number of post-PhD citation counts and H-index. Our results are most relevant to early stage pre-PhD researchers and emphasize the importance of building a publication track record. We show that a PhD obtained at a younger age is an outstanding advantage in the later scientific career prognosticating not only more abundant publications but also higher impact of these as measured by citation counts. Post-PhD research output shows a strong correlation to the number of publications and their impact during PhD studies in all scientific disciplines. Our results emphasize the need for pre-doctoral training programs having an emphasis on regular publications. The listed scientific discipline-specific values of scientometric parameters at the time of obtaining the PhD can help to select the most suitable applicants for postdoctoral grants and positions.

Supporting information

Mean, standard deviation, and 95% CI for the age, number of publications in the previous five years, H-index, and yearly independent citations in each scientific section at the year of obtaining the PhD (A), and ten years after obtaining the PhD (B).

Acknowledgments

The authors acknowledge the support of ELIXIR Hungary ( www.elixir-hungary.org ).

Funding Statement

The research was financed by the 2018-2.1.17-TET-KR-00001 and 2018-1.3.1-VKE-2018-00032 grants and by the Higher Education Institutional Excellence Programme (2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Bionic thematic program of the Semmelweis University.

Data Availability

Education school launches online doctorate in leadership

The UNC School of Education’s program for aspiring leaders across sectors will host its first cohort of students in fall 2024.

With approval from the UNC System, the UNC School of Education has launched the University’s first fully online doctoral degree program — the Doctor of Education in organizational learning and leadership.

The program was created and delivered in collaboration with The Graduate School at UNC-Chapel Hill, the UNC Office of Digital and Lifelong Learning and 2U. It will provide working professionals with knowledge, skills and experiences to take a human-centered approach to leadership, delivering upon their organization’s goals and mission.

Applications are open as of March 20, and the program’s first cohort of students will begin classes in fall 2024.

“For more than 130 years, the UNC School of Education has prepared leaders for classrooms, schools and school districts but also institutions well beyond education,” said Fouad Abd-El-Khalick, education dean and Alumni Distinguished Professor. “Education happens lifelong and lifewide, and learning happens within successful organizations. Our faculty members have expertise in areas critical to organizational learning and leadership, and we believe this program is an incredible opportunity for professionals to advance their careers and organizations.”

Developed with input from working professionals in both private and public sectors and spanning various industries, the new doctoral program is tailored to equip students with specialized courses in improvement science, change leadership, human-centered design, equitable and inclusive practice, research methodology and more. It will also offer students practical opportunities to implement their learning directly into their professional endeavors.

The program will use a blend of interactive online classes, self-paced asynchronous lessons, and an in-person immersion experience on the UNC-Chapel Hill campus. During the immersion experience, students will have the chance to forge meaningful connections with faculty, staff and fellow students.

The program culminates with a capstone project in which students identify a complex or pervasive challenge faced by their organization. Students will then undertake comprehensive research and implementation efforts to address it and design effective resolutions.

“Our faculty members have intentionally shaped this program and designed its curriculum to prepare professionals with tools to provide immediate value for our students, for the teams they lead or are part of, and for their organizations,” said Thurston Domina, associate dean for academic affairs and director of graduate studies at the school. “When our students graduate, we anticipate they will find success in private companies, government and nonprofit organizations, business and management consulting, human resources, project management, leadership coaching or development, and more.”

“Leadership of teams and organizations is a complex endeavor,” said Warren, “and we are excited to welcome students from an array of career stages, roles, organizations and locations who are committed to that endeavor. I look forward to cultivating robust learning environments where our students and equally committed and expert faculty members engage in ways that ultimately work toward the greater good through human-centered leadership practice.”

Prospective students can begin the online application at online.unc.edu . The priority deadline for the first cohort of students is June 11, while the final deadline is July 3. Details about application requirements are available online or by contacting an admissions counselor.

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PhD Student (index no. 6700-01)

Job information, offer description.

The Paul Scherrer Institute PSI is the largest research institute for natural and engineering sciences within Switzerland. We perform cutting-edge research in the fields of future technologies, energy and climate, health innovation and fundamentals of nature. By performing fundamental and applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations. Therefore, about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether, PSI employs 2200 people.

For the SNF Sinergia Project SUGAR we are looking to hire a PhD student with expertise in solid state devices, photonics and nanofabrication. The project focuses on developing group IV (SiGe, GeSn) active photodinc devices, with a focus on energy efficiency and sustainability.

For the Laboratory for Nano Quantum Technologies we are looking for a

PhD Student for photonic device design, fabrication and characterization

• Design and fabricate electro-optic devices based on germanium compounds grown at EPFL
• Carry out electrical and optical characterization of fabricated devices, and further support the development of new characterization set-ups
• Develop strategies for light coupling to our devices
• Evaluate strain enhancement techniques to improve the emission of light from GeSn
• Presentation of results at conferences and publication in relevant journals
• You will be enrolled in the doctoral program at Ecole Polytechnique Federale de Lausanne (EPFL) and must follow the courses required to obtain your PhD degree, as well as support teaching activities

Requirements

Your profile

• Master’s degree in electrical engineering, physics or a related field
• A solid understanding of solid state devices and physics
• Excellent written and spoken English language skills
• Experience with nanofabrication, optical and/or electrical characterization
• You should be able to communicate well, work in a team and possess good presentation skills

Additional Information

We offer Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure.

Please submit your application online by 9 April 2024 (including addresses of referees) for the position as a PhD Student (index no. 6700-01).

Paul Scherrer Institut, Human Resources Management, Noémie Lushaj, 5232 Villigen PSI, Switzerland, www.psi.ch

Work Location(s)

Where to apply.

Center for Public History

• College of Liberal Arts and Social Sciences

2024 Graduate Program Manager for Summer Internship Program

Mailing Address

Center for Public History University of Houston Science and Research 2 3455 Cullen Blvd., Room 521 Houston, TX 77204-3007 (713) 743-3087

Job Description: The summer graduate program manager will work closely with the faculty director in supervising and reporting on student participation, progress, and goal setting during the summer internship. The summer graduate program manager will be on campus each Wednesday for weekly workshops and presentations. The summer graduate program manager will also be responsible for developing curriculum and facilitating a weekly Wednesday cohort discussion, which reflects on the larger programming of the week and helps interns workshop their experiences on individual faculty projects. The summer graduate program manager will guide students in the final public presentation of their work.    

Dates: The internship program will run from May 29-July 31, 2024. The summer graduate program manager will begin working with the Internship Director on May 15th to prepare for the student and faculty orientation on May 22. The summer graduate program manager will also help write a final report in early August with the position ending on August 7. This position requires 20 hours per week. 

Payment: The graduate summer program manager will be paid $4,000 for the summer, which will be issued through the Scholarship Award Memo system.     

Eligibility:  All current UH graduate students encouraged to apply. We especially welcome students from public history, digital humanities, and related fields with teaching or leadership experience.   

Questions?  For more information, contact Dr. Kristina Neumann at [email protected] .  

Deadline: Applications are due by April 15, 2024.   

To apply, click here. Application materials include: 

• Letter of interest highlighting related leadership, teaching and organizational experience 
• Current resume or CV  

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‘Scary and Daunting’: Dartmouth Players Detail How Union Plan Came Together

On Tuesday, the historic 13-2 vote by the men’s basketball team to unionize took a significant step toward classifying student-athletes as employees.

By Billy Witz

Billy Witz reported from Hanover, N.H.

Members of the Dartmouth College men’s basketball team congregated at the stately Hanover Inn near campus on a dreary, drizzly Tuesday and walked over to a small office building where they smiled for a group photo. Then they went up to a second-floor conference room and took a vote that had been six months — or rather, many years — in the making.

When the yellow sheets of paper were tallied and certified about an hour later, the basketball players had accomplished something no other college athletes had done.

By a 13-2 vote, they had formed a union.

“It’s definitely becoming more real,” Cade Haskins, a junior on the basketball team and a leader of the effort, said to about a dozen reporters after the vote. “We know this could potentially be making history. That wasn’t the reason we were doing it, but to do that can be scary and daunting.”

Haskins expressed hope that his peers across the Ivy League and the rest of the country would soon be recognized as employees under federal labor law — a classification that has been a red line for college sports leaders who would be forced to share revenue directly with athletes.

But at a time when college sports’ amateur model is buckling under the strain of antitrust lawsuits, unfair labor challenges and waning support in Congress, it is unclear whether Tuesday’s election will be remembered as a signature moment or a footnote.

There is no visible movement to organize by other Dartmouth teams. And a reminder that the case is far from final arrived just before the vote: Dartmouth filed an appeal of a regional director’s decision last month to classify the players as employees to the full National Labor Relations Board, which has jurisdiction only over private employers.

(Nearly a decade ago, a regional director awarded the Northwestern football team the right to vote to form a union , but when the board declined to assert jurisdiction in the case , the votes, which had been impounded, were destroyed before they could be counted.)

Dartmouth could eventually take the board’s decision to a federal appellate court, meaning that the case may not be resolved until the current players have graduated.

In a statement, the college called the unionization vote inappropriate: “Classifying these students as employees simply because they play basketball is as unprecedented as it is inaccurate.”

Also on Tuesday, a House of Representatives subcommittee announced a hearing next week titled “Safeguarding Student-Athletes from N.L.R.B. Misclassification.”

When asked how far the Dartmouth players were from the finish line, Haskins said, “We’re closer than we started.”

The vote is the latest flex by organized labor, whose nationwide activity — and popularity — has, with the backing of the Biden administration, surged to levels not seen since the 1960s.

Still, Dartmouth is a somewhat unlikely hub of activism. It does not have a rich history of rabble rousing like the University of California, Berkeley. The war in Gaza has not roiled the campus to the extent that it has at other Ivy League schools. The school is in a remote location and has the smallest enrollment in the Ivy League (4,556 undergraduates), providing organizers with only so much oxygen in a place whose independent streak is imbued in the state motto: Live Free or Die.

Yet, the basketball team is just the latest Dartmouth group to organize in the last two years, following student workers, graduate student workers and library workers. The dormitories’ resident advisers are in the process of forming a union.

“The last few years have been a whirlwind of labor activity in this tiny, rural place,” said Marc Dixon, the chairman of the Sociology department, who studies labor issues. “The pace has been really wild.”

Perhaps not surprisingly, this local wave of activity had its roots in the coronavirus pandemic.

When Dartmouth students returned to campus under a hybrid schedule in the fall of 2020, students who worked at the two campus eateries felt stuck. They needed the $11-per-hour jobs, but they also felt especially vulnerable to the virus.

Around the time when the food service workers began to organize, their effort received a boost: Dartmouth announced in the fall of 2021 that its endowment had generated a whopping 46-percent return in the previous fiscal year, climbing to $8 billion. (Dartmouth said at the time that it would raise its minimum wage from $7.75 to $11.50.)

About six months later, the food-service workers had voted to unionize.

When negotiations with the college lagged, the workers voted to strike in February 2023. Dartmouth immediately relented — bumping food service workers pay to $21 per hour, along with agreeing to sick pay for Covid-19 and overtime for late-night shifts.

“As a freshman, you’re not in position to get a research job,” said Ian Scott, a senior who worked in the dish room at a campus cafe and was an organizer. “Dining service is where you go when you can’t be choosy. Many people who work there were — and still are — low-income people of color who need aid.”

Watching this play out was Haskins, who worked at a dining hall. He also plays basketball. (About half of the team members have jobs at the school.)

Haskins, a junior from Minneapolis who is a policy, philosophy and economics major, had struck up a friendship with Walter Palmer, a former Dartmouth player who works in the alumni office. Palmer, who remains the most recent Dartmouth player to be drafted by the N.B.A., in 1990, helped form the first players’ union in Europe and has also worked for the N.B.A. Players Association. He connected the players with the local Service Employees International Union — and other influential figures like Tony Clark, the head of the Major League Baseball Players Association.

Soon plans were made for taking their case to the N.L.R.B. in September, after the three freshmen on this year’s team arrived. (Haskins and Romeo Myrthil, a junior from Solna, Sweden, who is studying computer science, were viewed as ideal leaders because they would not graduate until next year.)

“We take an oath to organize the unorganized, but it doesn’t really say what that means,” said Chris Peck, a painter who is the longtime president of Local 560. “College athletes — how does that fit? You assume they come from money and they’ve got the world by the tail. Then you hear that they’re working jobs on top of going to practice and studying. It was a similar story as the dining workers.”

This case, though, does not fit neatly into any box.

Dartmouth, like the rest of the Ivy League schools, does not offer athletic scholarships — only need-based financial aid. And the basketball team did not reap tens of millions like Kansas or Kentucky. In fact, it is subsidized by Dartmouth, which has incurred more than $3.2 million in losses operating the program over the last five years, according to testimony at the hearing. (Distributions from the N.C.A.A. men’s basketball tournament and the Ivy League television contract with ESPN are categorized as athletic department revenue.)

In granting the players employee status, the regional director hearing the case, Laura A. Sacks, ruled that the six pairs of basketball shoes (valued at $200 apiece) given to players each season and the two to four tickets that players are provided to each game for their family and friends served as compensation and thus place the players under the college’s control.

She also ruled that another form of compensation is access to the “early read” admissions process because of their value as basketball players.

Those are among the issues that Dartmouth, which recently hired the same lawyers who are representing the University of Southern California in an N.L.R.B. case asserting that football and men’s and women’s basketball players are employees, is pushing back against in its appeal to the full board. The law firm, Morgan Lewis, also represents SpaceX, Amazon and Trader Joe’s, companies that have challenged the authority of the N.L.R.B.

While there seems to be general support for the basketball players, there does not seem to be widespread eagerness on campus to take on the hard work of organizing athletes in many of the other 33 sports that Dartmouth sponsors.

New rules allowing athletes to make money from endorsements has prompted them to think about their circumstances, a member of the men’s hockey team said.

“I think guys are comfortable with the way things are,” said the player, who asked to not be identified because he had not received authorization from Dartmouth to speak with the news media. “We get to play hockey and go to a school that we’re super thrilled about. It’s a choice we make to come here, and so you accept the pros and the cons.”

He also noted that the team is having its best season in nearly a decade.

That is not the case with the men’s basketball team, which has had a desultory season, anchored in last place in the Ivy League. But when the Big Green staged a spirited rally to defeat Harvard on Tuesday night, it allowed them to conclude their 6-21 season with a smile — and a second victory on the day.