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Open Access journal publishing debates – the elephant in the room?

Sunday, November 4th, 2018

For perhaps ten years now, the future of scientific publishing has been hotly debated. The traditional models are often thought to be badly broken, although convergence to a consensus of what a better model should be is not apparently close. But to my mind, much of this debate seems to miss one important point, how to publish data.

Thus, at one extreme is COAlition S, a model which promotes the key principle that “after 1 January 2020 scientific publications on the results from research funded by public grants provided by national and European research councils and funding bodies, must be published in compliant Open Access Journals or on compliant Open Access Platforms.” This includes ten principles, one of which “The ‘hybrid’ model of publishing is not compliant with the above principles” has revealed some strong dissent, as seen at forbetterscience.com/2018/09/11/response-to-plan-s-from-academic-researchers-unethical-too-risky I should explain that hybrid journals are those where the business model includes both institutional closed-access to the journal via a subscription charge paid by the library, coupled with the option for individual authors to purchase an Open Access release of an article so that it sits outside the subscription. The dissenters argue that non-OA and hybrid journals include many traditional ones, which especially in chemistry are regarded as those with the best impact factors and very much as the journals to publish in to maximise both the readership, hence the impact of the research and thus researcher’s career prospects. Thus many (not all) of the American Chemical Society (ACS) and Royal Society of Chemistry (RSC) journals currently fall into this category, as well as commercial publishers of journals such as Nature, Nature Chemistry,Science, Angew. Chemie, etc. 

So the debate is whether funded top ranking research in chemistry should in future always appear in non-hybrid OA journals (where the cost of publication is borne by article processing charges, or APCs) or in traditional subscription journals where the costs are borne by those institutions that can afford the subscription charges, but of course also limit the access.  A measure of how important and topical the debate is that there is even now a movie devoted to the topic which makes the point of how profitable commercial scientific publishing now is and hence how much resource is being diverted into these profit margins at the expense of funding basic science.

None of these debates however really takes a close look at the nature of the modern research paper. In chemistry at least, the evolution of such articles in the last 20 years (~ corresponding to the online era) has meant that whilst the size of the average article has remained static at around 10 “pages” (in quotes because of course the “page” is one of those legacy concepts related to print), another much newer component known as “Supporting information” or SI♥ has ballooned to absurd sizes. It can reach 1000 pages[1] and there are rumours of even larger SIs. The content of SI is of course mostly data. The size is often because the data is present in visual form (think spectra). As visual information, it is not easily “inter-operable” or “accessible”. Nor is it “findable” until commercial abstracting agencies chose to index it. Searches of such indexed data are most certainly “closed” (again depending on institutional purchases of access) and not “open access”. You may recognise these attributes as those of FAIR (Findable, accessible, inter-operable and re-usable). So even if an article in chemistry is published in pure OA form, in order to get FAIR access to the data associated with the article, you will probably have to go to a non-OA resource run by a commercial organisation for profit. Thus a 10 page article might itself be OA, but the full potential of its 1000+ page data (an elephant if ever there was one) ends up being very much not OA.

You might argue that the 1000+ pages of data does not require the services of an abstracting agency to be useful. Surely a human can get all the information they want from inspecting a visual spectrum? Here I raise the future prospects of AI (artificial intelligence). The ~1000 page SI I noted above[1] includes e.g NMR spectra for around 70 compounds (I tried to count them all visually, but could not be certain I found them all). A machine, trained to identify spectra from associated metadata (a feature of FAIR), could extract vastly more information than a human could from FAIR raw data‡ (a spectrum is already processed data, with implied information/data loss) in a given time. And for many articles, not just one. Thus FAIR data is very much targeted not only at humans but at the AI-trained machines of the future.

So I again repeat my assertion that focussing on whether an article is OA or not and whether publishing in hybrid journals is to be allowed or not by funders is missing that 100-fold bigger elephant in the room. For me, a publishing model that is fit for the future should include as a top priority a declaration of whether the data associated with it is FAIR. Thus in the Plan-S ten principles, FAIR is not mentioned at all. Only when FAIR-enabled data becomes part of the debates can we truly say that the article and its data are on its way to being properly open access.

‡The FAIR concept did not originally differentiate between processed data (i.e. spectra) and the underlying primary or raw data on which the processed data is based. Our own implementation of FAIR data includes both types of data; raw for machine reprocessing if required, and processed data for human interpretation. Along with a rich set of metadata, itself often created using carefully designed workflows conducted by machines.

♥The proportion of articles relating to chemistry which do not include some form of SI is probably low. These would include articles which simply provide a new model or interpretation of previously published data, reporting no new data of their own. A famous historical example is Michael Dewar’s re-interpretation of the structure of stipitatic acid[2] which founded the new area of non-benzenoid aromaticity.

References

  1. J.M. Lopchuk, K. Fjelbye, Y. Kawamata, L.R. Malins, C. Pan, R. Gianatassio, J. Wang, L. Prieto, J. Bradow, T.A. Brandt, M.R. Collins, J. Elleraas, J. Ewanicki, W. Farrell, O.O. Fadeyi, G.M. Gallego, J.J. Mousseau, R. Oliver, N.W. Sach, J.K. Smith, J.E. Spangler, H. Zhu, J. Zhu, and P.S. Baran, "Strain-Release Heteroatom Functionalization: Development, Scope, and Stereospecificity", Journal of the American Chemical Society, vol. 139, pp. 3209-3226, 2017. https://doi.org/10.1021/jacs.6b13229
  2. M.J.S. DEWAR, "Structure of Stipitatic Acid", Nature, vol. 155, pp. 50-51, 1945. https://doi.org/10.1038/155050b0

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PIDapalooza 2018. A conference like no other!

Tuesday, January 23rd, 2018

Another occasional conference report (day 1). So why is one about “persistent identifiers” important, and particularly to the chemistry domain?

The PID most familiar to most chemists is the DOI (digital object identifier). In fact there are many; some 60 types have been collected by ORCID (themselves purveyors of researcher identifiers). They sometimes even have different names; in life sciences they tend to be known instead as accession numbers. One theme common to many (probably not all) is that they represent sources of metadata about the object being identified. Further information if which allows you (or a machine) to decide if acquiring the full object is worthwhile. So in no particular order, here are some of the things I learnt today.

  1. Mark Hahnel noted the recent launch of the Dimensions resource which links research data with other research activities; I have not yet had a chance to learn its capabilities, but it seems an interesting alternative to other stalwarts such as eg Google Scholar etc.

    You can try this example: https://app.dimensions.ai/discover/publication?search_text=10.6084&search_type=kws&full_search=true which retrieves articles in which the data repository with prefix 10.6084 (Figshare) is cited. Try also the prefix 10.14469 which is the Imperial College repository.

  2. Andy Mabbett talked about the deployment and use of persistent identifiers (the Q numbers) in Wikidata, which increasingly underpin the basis for the various flavours of Wikipedia. He also noted their use of some 50 different identifiers.
  3. Johanna McEntyre noted some 5M published articles in life sciences which reference 1M+ ORCID identifiers, easily the domain with the fastest uptake of this type. Also noted was the new FREYA project; aiming to connect open identifiers for discovery, access and use of research resources.
  4. Tom Gillespie talked about RRID, or Research Resource Identifiers. Included in this are hardware, including instruments and with around 6000 RRIDs systematized so far. They argue this area promotes both the A and I of FAIR (accessible and inter-operable). Of course A and I mean many things to many people.
  5. Several other presentations talked about the finer detail of metadata, such as sub-classifications into e.g. descriptive/admin/technical, but I did rather miss demos showing how search queries of such fine-grained metadata could be constructed.

Apart from the presentations themselves, PIDapalooza is unusual for some other activities. Thus you could go get your PIDnails done, with a selection of 8 or so tasteful logos to choose from. There will be tattoos tomorrow (this is a conference for younger people after all). I may grab a photo or two to provide evidence!

 

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500 chemical twists: a (chalk and cheese) comparison of the impacts of blog posts and journal articles.

Friday, June 3rd, 2016

The title might give it away; this is my 500th blog post, the first having come some seven years ago. Very little online activity nowadays is excluded from measurement and so it is no surprise that this blog and another of my “other” scholarly endeavours, viz publishing in traditional journals, attract such “metrics” or statistics. The h-index is a well-known but somewhat controversial measure of the impact of journal articles; here I thought I might instead take a look at three less familiar ones – one relating to blogging, one specific to journal publishing and one to research data.

First, an update on the accumulated outreach of this blog over this seven-year period. The total number of country domains measured is 190. The African continent still has quite a few areas with zero hits (as does Svalbard, with a population of only 2600 for a land mass area 61,000 km2 or 23 km2 per person). Given the low blog readership density on the African continent, it would be interesting to find out whether journal readership is any better.

Next, I look at the temporal distribution for individual posts. The first has attracted the highest total; in five years it has had 19,262 views (the diagram below shows the number of views per day). Four others exceed 10,000 and 80 exceed 1000 views.

Of these five, the next is the oldest, going back to 2009. I was very surprised to find such longevity, with the number of views increasing rather than decreasing with the passage of time.

So time now to compare these statistics with the journals. And of course its chalk and cheese. A “view” for a post means someone (or something) accessing the post URL, which is then recorded in the server log. Resolving the URL does at least load the entire content of the post; whether its read or not is of course not recorded. Importantly, if you want to view the content at some later stage, a new “view” has to be made (although some browsers do save a web page and allow offline viewing at a later stage, but I suspect this usage is low). With electronic journal access, it’s rather different. Access to an article is now predominantly via two mechanisms:

  1. From the table of contents (this is somewhat analogous to browsing a blog)
  2. From the article DOI.

Statistics for these two methods are gathered differently. The new CrossRef resource chronograph.labs.crossref.org (CrossRef allocate all journal DOIs) can be used to measure what they call DOI “resolutions”. A DOI resolution however leads one only to what is called the “landing page”, where the interested reader can view the title, the graphical abstract and some other metadata. It does not mean of course that they go on to actually view the article (as HTML, equivalent to the blog above, or probably more often by downloading a PDF file). Here are a few results using this method:

What about the other main journal article access method, not via a DOI but from a table of contents page journal page? A Google search revealed this site: jusp.mimas.ac.uk (JUSP stands for Journal usage statistics portal, which sounded promising). This site collects “COUNTER compliant usage data”. COUNTER (Counting Online Usage of Networked Electronic Resources) is an initiative supported by many journal publishers and it sounds an interesting way of measuring “usage” (as opposed to “views” or “resolutions”; it’s that chalk and cheese again!). I would love to be able to show you some statistics using this resource, but the “small print” caught me out: “JUSP gives librarians a simple way of analysing the value and impact of their electronic journals”. Put simply, I am a researcher, not a librarian. As a researcher I do not have direct access; JUSP is a closed, restricted access (albeit taxpayer-funded) resource. I am discussing this with our head of information resources (who is a librarian) and hope to report back here on the outcome.

Finally research data. This is almost too new to be able to measure, but this resource stats.datacite.org is starting to collect statistics on data resolutions (similar to DOI resolutions).

  1. You can see from the below for Imperial College (in fact this represents the two data repositories that we operate and which I cite here extensively on these blogs) that the resolution at running up to about 200 a month per dataset (more typically ~25 a month), with a total of 5065 resolutions for all items in March 2016 (the blog has ~12,000 views per month).

  2. Figshare is another data repository we have made use of:

So to the summary.

  1. Firstly, we see that I have shown three forms of impact, views, resolutions and usage. If one had statistics on all three, one might then try to see if they are correlated in any way. Even then, normalisation might be a challenge.
  2. Over ~7 years, five posts on this blog have attracted >10,000 views.
  3. Many of the blog posts have a long “finish” (to use a wine tasting term); the views continue regularly and often increase over time.
  4. My analysis of the three journal articles above (and about 15 others) shows that between 50-300 resolutions over a few years is fairly typical (for this researcher at least; I am sure most better known researchers attract far far more).
  5. The temporal distribution for article resolutions and blog views show both can have continuing impact over an extended period. None of the 18 articles I looked at show a significantly increasing impact with time but many of the blog posts do. This tends to suggest that the audiences for each are quite different; researchers for articles and a fair proportion of inquisitive students for the blog?
  6. I may speculate whether a correlation between my article resolutions and my h-index probably might be found, but the article resolution has a fine-grained temporal resolution (allowing a derivative wrt time to be obtained) that is perhaps potentially more valuable than just the coarse h-index integration (an article can of course be cited for both positive and negative reasons!).
  7. Initial analysis for data shows resolutions running at a similar rate to article resolutions. It is not yet possible to correlate data resolutions with article resolutions in which that data is discussed.

References

  1. S.M. Rappaport, and H.S. Rzepa, "Intrinsically Chiral Aromaticity. Rules Incorporating Linking Number, Twist, and Writhe for Higher-Twist Möbius Annulenes", Journal of the American Chemical Society, vol. 130, pp. 7613-7619, 2008. https://doi.org/10.1021/ja710438j
  2. A.E. Aliev, J.R.T. Arendorf, I. Pavlakos, R.B. Moreno, M.J. Porter, H.S. Rzepa, and W.B. Motherwell, "Surfing π Clouds for Noncovalent Interactions: Arenes versus Alkenes", Angewandte Chemie International Edition, vol. 54, pp. 551-555, 2014. https://doi.org/10.1002/anie.201409672
  3. K. Abersfelder, A.J.P. White, H.S. Rzepa, and D. Scheschkewitz, "A Tricyclic Aromatic Isomer of Hexasilabenzene", Science, vol. 327, pp. 564-566, 2010. https://doi.org/10.1126/science.1181771

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Data-free research data management? Not an oxymoron.

Tuesday, May 24th, 2016

I occasionally post about "RDM" (research data management), an activity that has recently become a formalised essential part of the research processes. I say recently formalised, since researchers have of course kept research notebooks recording their activities and their data since the dawn of science, but not always in an open and transparent manner. The desirability of doing so was revealed by the 2009 "Climategate" events. In the UK, Climategate was apparently the catalyst which persuaded the funding councils (such as the EPSRC, the Royal Society, etc) to formulate policies which required all their funded researchers to adopt the principles of RDM by May 2015 and in their future researches. An early career researcher here, anxious to conform to the funding body instructions, sent me an email a few days ago asking about one aspect of RDM which got me thinking.

The question related to the divide between data as a separate research object (and which therefore has to be managed), and data as an inseparable part of the article narrative, which is of course ostensibly managed by the journal publication processes. Such data may often be the description of a process rather than simply tables of numbers or graphs. In chemistry it may include chemical names and chemical terms as part of an experimental procedure. For one nice illustration of such embedded data, go look at the chemical tagger page. Here the data is blending with the semantics, and the two are not easily separated. So, when such separation is not easily achieved, should the specific processes required by RDM as illustrated in the five bullet points below actually be followed?

  1. Specify a data management plan to be followed, as for example points 2-5 below.
  2. Decide upon a location for your data, separated into one for "live" or working data (the purpose simply being to ensure it is properly backed up) and the other for a sub-set of formally "published data" which has to be available for at least ten years after its publication.
  3. Use 2 to gather metadata (see 6-14 below) and in return get a DOI representing the location of the combined metadata + data, from a suitable registration authority such as DataCite.
  4. Quote this DOI(s) in the article describing the results of analysing the data and presenting hypotheses, and conversely once the article itself is allocated its own DOI from a registration authority such as CrossRef, update the metadata in item 3 so as to achieve a bidirectional link between the data and its narrative (and we assume that DataCite and CrossRef will also increasingly exchange the metadata they each hold about the items).
  5. Add both the data and the article DOIs to any institutional CRIS or current research information system (parenthetically, I regard this last stipulation as rather redundant if items 3 and 4 are working effectively, but its a good interim measure whilst the overall system matures).

So, should step 2 be included if the data itself is inextricably intertwined with the narrative and cannot be separated? The slightly surprising advice I would suggest is yes! And the answer is that it IS possible to generate metadata (data about the, possibly entwined, data) which CAN be processed in such a step. What forms would such metadata take?

  1. Identification of the researcher(s) involved. This would nowadays take the form of an ORCID (Open Research and Collaborator Identifier).
  2. Identification of the hosting institution where the data has been produced. There is currently no equivalent to an ORCID for institutions, but it is very likely to come in the future.
  3. A date stamp formalising when the (meta)data is actually deposited.
  4. A title for the project being described. Here we see a blurring between the narrative/article and the data; a title is the shortest possible description of the narrative/article, and it may also apply to the data object(s) or it could have its own title.
  5. A slightly fuller abstract of the project being described. Here we see further blurring between the narrative and data objects.
  6. One can include "related identifiers", in particular the DOIs of any other relevant articles that might have been published which may expand the context of the data, and also the DOIs of any other relevant datasets which may have been allocated in step 2 above.
  7. It is also beneficial to include "chemical identifiers". These can take the form of InChI strings and InChI keys, which allow discretely defined molecular objects which were the object of the research to be tracked and which relate to both the narrative and any other data objects.
  8. If specific software has been used to analyse data, it too can be included as a "related identifier" (e.g. [1]
  9. Potentially at least, if a well-defined instrument has been involved, it too could be included with its own "related identifier". With both 13-14, other issues may need addressing, such as versioning etc, but this no doubt will be sorted in due course.
  10. etc.

So items such as 6-14 can be collected and sent to e.g. DataCite with a DOI received in return as part of item 2 of the RDM processes. No "pure" data need be involved, only metadata. Nonetheless such metadata can only increase the visibility and discoverability of the research, as illustrated in how such metadata can be searched for the components described above.

References

  1. H.S. Rzepa, "KINISOT. A basic program to calculate kinetic isotope effects using normal coordinate analysis of transition state and reactants.", 2015. https://doi.org/10.5281/zenodo.19272

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LEARN Workshop: Embedding Research Data as part of the research cycle

Monday, February 1st, 2016

I attended the first (of a proposed five) workshops organised by LEARN (an EU-funded project that aims to ...Raise awareness in research data management (RDM) issues & research policy) on Friday. Here I give some quick bullet points relating to things that caught my attention and or interest. The program (and Twitter feed) can be found at https://learnrdm.wordpress.com where other's comments can also be seen. 

  • Henry Oldenburg, founder member and first secretary of the Royal Society, was the first Open Scientist.
  • About 100 people attended the workshop. Of these ~3-5 identified themselves as researchers creating data, and the rest comprised research data managers, administrators, librarians, publishers (but see below) etc. Many were new to their posts.
  • Not publishing scientific data should become recognised as scientific malpractice.
  • Central libraries should pro-actively disperse their knowledge to data scientists in departments.
  • If a scientist is concerned that openly publishing their data might give advantage to their competitors, they are urged to counteract this by "being cleverer than the others". 
  • The three great bastions of open science are (a) Open Data, (b) Open access articles and (c) doing science openly. Examples of this third category include open notebook science (ONS), a form notably pioneered by Jean-Claude Bradley. One attribute of ONS was noted as no insider knowledge.
  • Learned societies should endow medals for Open Science.
  • (Some) publishers are reinventing themselves as Research Facilitators.

The plenaries are all well worth dipping into (certainly the video and in some cases all the slides are scheduled to appear).

If you are a researcher (undergraduate students, PGs, PDRAs, early career researchers and academics) you should immediately track down your local evangelist/expert in RDM and ask what the local infrastructures are (or will be shortly built). 

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Personal web pages on digital repositories.

Saturday, June 20th, 2015

The university sector in the UK has quality inspections of its research outputs conducted every seven years, going by the name of REF or Research Excellence Framework. The next one is due around 2020, and already preparations are under way! Here I describe how I have interpreted one of its strictures; that all UK funded research outputs (i.e. research publications in international journals) must be made available in open unrestricted form within three months of the article being accepted for publication, or they will not be eligible for consideration in 2020.

At the outset, I should say that one infrastructure to help researchers adhere to the guidelines is being implemented in the form of the Symplectic system. This allows a researcher to upload the final accepted version of a manuscript. At Imperial College, a digital repository called Spiral serves this purpose and also acts as the front end for collecting informative metadata to enhance discoverability. The final accepted version is then converted by the publisher into a version-of-record. This contains styling unique to the publisher and the content is subjected to further scrutiny by the authors as proof corrections. In an ideal world, these latter changes should also be faithfully propagated back to the final accepted version, as would all the supporting information associated with the article. Since most authors do not exactly enjoy the delights of proof corrections, this final reconciliation of the two versions may not always be assiduously undertaken.

I became concerned about the existence of two versions of any given scientific report and that the task of ensuring total fidelity in the content of both versions may negatively impact on the author’s time. Much better if the publisher could grant permission for the author to archive the version-of-record into a digital repository.

Some experiments were needed, and I decided to start them in reverse, by archiving my oldest publications. Since Symplectic now provides a system to do this, I began by using it. Symplectic identifies each publisher’s policies for archival, of which the most liberal are known as ROMEO GREEN. To quote from the definition, this colour allows the author to “archive pre-print and post-print or publisher’s version/PDF“. In an afternoon I had processed most of my ROMEO green articles. You know how it is sometimes, you do not read the fine print! And so the library soon informed me that archival of ROMEO GREEN was in fact only permitted on the author’s “personal web page”. Spiral, as an institutional repository, does not apparently constitute a personal web page for me and so none of my Symplectic submissions could be accepted for archival there.

Time to rethink the experiment. Firstly, I very much wanted the reprints to be held by a proper digital repository rather than a conventional web page. Why? I wanted my reprints to adhere as much as possible to FAIR: findable, accessible, interoperable and re-usable. Well, at least the first two of those (the last two relate more to data). A repository is designed to hold metadata in a formal and standards-based manner and metadata helps achieve FAIR. So I asked the Royal Society of Chemistry (as a ROMEO GREEN publisher) whether a personal web page hosted on a digital repository would qualify. I was soon informed  that I had proposed a neat solution here, and they couldn’t see an issue.

Now, all I had to do is find a repository where I could create such a personal web page. The chemistry department at Imperial College has for ten years hosted a DSpace repository called SPECTRa[1] which already has the functionality for individuals to create personal collections. I had also picked up on the increasing attention being given to Zenodo, like the World-Wide Web itself an offshoot of CERN (of large Hadron Collider fame) and born from the need for researchers to more permanently archive the outputs of their researches. These outputs include software, videos, images, presentations, posters, publications and (most obviously for CERN) datasets. I thought I would include them in my experiment as well. There results are summarised below.

DSpace-SPECTRa Zenodo
Community Henry Rzepa personal web page
reprint collection		 Rzepa personal computational
chemistry data and reprint page
Collection Royal Society of Chemistry reprints
Publication 10042/195577 10.5281/zenodo.18758[2]
Thesis 10044/1/20860[3] 10.5281/zenodo.18777[4]
Dataset 10.14469/ch/191342[5] 10.5281/zenodo.18632[6]
Harvesting OAI-ORE OAI-PMH

The last line of this table includes a link to another design feature of a repository, facilitating the ability to harvest the content. The ContentMine project (“The right to read is the right to mine!“) has shown how such harvesting of facts from the literature can be automated on a vast scale, and (IMHO) represents an example of those disruptive innovations that have the power to change the world forever. It also enshrines the idea that scientific facts funded by the public purse should be capable of being openly liberated from their containers. A harvestable repository seems an ideal container for achieving this.

My experiment is part of what might be seen as the increasingly subtle interplay between:

  • scientific authors, whose creative endeavour research is and without whom scientific publishers would not exist
  • publishers who create a business model from the content freely given them by authors but also (especially if a commercial publisher) need to be accountable to their shareholders.
  • the funding councils, many of whom now wish the outcomes of the research they fund to be openly available to all
  • the local libraries/administrators who have to adhere to/enforce all the rules contractually handed down to them by publishers whose direct customers they are, but who also need to serve their community of readers and authors.
  • researchers who would rather do research than fret about the above, and who would rather spend limited resources doing that research rather than diverting an increasing amount of their attention into the above system.
  • readers, who need unimpeded access to the research endeavours of others, but often have little influence on the policies and actions of all the other stakeholders, since they are  NOT considered customers (of the publishers).
  • etc. etc.

My experiment was in part designed to explore these rules, their interpretations and their boundaries. For the time being at least I seem to have found an arrangement that allows me to distribute versions-of-record of my own work, thanks to a generous and far-sighted learned society publisher. Watch this space!

References

  1. J. Downing, P. Murray-Rust, A.P. Tonge, P. Morgan, H.S. Rzepa, F. Cotterill, N. Day, and M.J. Harvey, "SPECTRa: The Deposition and Validation of Primary Chemistry Research Data in Digital Repositories", Journal of Chemical Information and Modeling, vol. 48, pp. 1571-1581, 2008. https://doi.org/10.1021/ci7004737
  2. H.S. Rzepa, and B.C. Challis, "The Mechanism Of Diazo-Coupling To Indoles And The Effect Of Steric Hindrance On The Rate Limiting Step", Zenodo, 1975. https://doi.org/10.5281/zenodo.18758
  3. H.S. Rzepa, "Hydrogen transfer reactions of indoles", 1974. http://doi.org/10044/1/20860
  4. H.S. Rzepa, "Hydrogen Transfer Reactions Of Indoles", Zenodo, 1974. https://doi.org/10.5281/zenodo.18777
  5. H.S. Rzepa, "C 25 H 34 Cl 1 N 3 O 1", 2015. https://doi.org/10.14469/ch/191342
  6. H.S. Rzepa, A. Lobo, M.S. Andrade, V.S. Silva, and A.M. Lourenco, "Chiroptical properties of streptorubin B – the synergy between theory and experiment.", 2015. https://doi.org/10.5281/zenodo.18632

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ORCID identifiers galore!

Tuesday, April 21st, 2015

Egon has reminded us that adoption of ORCID (Open researcher and collaborator ID) is gaining apace. It is a mechanism to disambiguate (a Wikipedia term!) contributions in the researcher community and to also remove much of the anonymity (where that is undesirable) that often lurks in social media sites.

This blog is now ORCID-enabled (my ORCID should appear at the top of this post for example, where you should be able to find it as , although the signature thumbnail orcid_24x24 is obscured by my gravatar, an older system for providing information about someone‡). We also add ORCIDs to all data depositions[1].

You will not yet find them in many journal articles, which was the whole original point of their introduction. They can however already be used to log into e.g. manuscript submission sites, for example the Journal of Chemoinformatics and I gather many other journal submission systems will probably start using it in 2015. From there it is short step to incorporating them into journal articles routinely.

To counter the slightly awkward association with “being reduced to a mere number”,† we need to start seeing genuine benefits from its pervasive use. From my point of view, there will be one immediate application. At my university we run a system called Symplectic, which in effect tracks all aspects of one’s research activities, including sourcing online publications. Each time Symplectic thinks it has found e.g. one of my articles, it sends me an email asking me to verify its discovery. I then have to spend 5 minutes or so acknowledging it was written by me, and then adding further links to e.g. instrumental resources used for that research. One of those resources is the high performance computing unit here. But since that resource already incorporates ORCID via e.g. [1], there is no reason why Symplectic need ever bother me with such questions in the future; it could automatically harvest all the information defined by my ORCID.

As with many steps forward, there are often steps back, following the law of unforeseen consequences. Perhaps “identity theft” is one; how easy could it be to use someone else’s ORCID for example? I think however that ORCID is here to stay, and we should explore both the good and the potentially bad aspects of its increasing deployment.

‡Gravatar offer a list of verified services similar in concept to ORCID. But ORCID itself is not on that list; http://en.gravatar.com/profiles/edit/#verified-services
†In the dystopian novel We by Yevgeny Zamyatin, there is no way of referring to people save by their given numbers. Wikipedia tells us We is considered  as having influenced the later novel 1984 by George Orwell.

References

  1. H.S. Rzepa, "C 6 H 6 Br 2", 2015. https://doi.org/10.14469/ch/191199

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Blasts from the past. A personal Web presence: 1993-1996.

Saturday, November 1st, 2014

Egon Willighagen recently gave a presentation at the RSC entitled “The Web – what is the issue” where he laments how little uptake of web technologies as a “channel for communication of scientific knowledge and data” there is in chemistry after twenty years or more. It caused me to ponder what we were doing with the web twenty years ago. Our HTTP server started in August 1993, and to my knowledge very little content there has been deleted (it’s mostly now just hidden). So here are some ancient pages which whilst certainly not examples of how it should be done nowadays, give an interesting historical perspective. In truth, there is not much stuff that is older out there!

  1. This page was written in May 1994 as a journal article, although it did have to be then converted into a Word document to actually be submitted.[1] Because it introduced hyperlinks to a chemical audience, we wanted to illustrate these in the article itself! Hence permission was obtained from the RSC for an HTML version to be “self-archived” on our own servers where the hyperlinks were supposed to work (an early example of Open Access publishing!). I say supposed because quite a few of them have now “decayed”. We were aware of course that this might happen, but back in 1994, no-one knew how quickly this would happen. What is interesting is that the HTML itself (written by hand then) has survived pretty well! I will leave you to decide how much the message itself has decayed.
  2. This HTML actually predates the above; it was written around November 1993 and represented the very first lecture notes I converted into this form (on the topic of NMR spectroscopy). A noteworthy aspect is the scarce use of colour images. At the start of 1994, the bandwidth available on our campus was pretty limited (the switches were 10 Mbps only) and a request went out to reduce the bit-depth of any colour images to 4-bits to help conserve that bandwidth! I rather doubt anyone took much notice however, and the policy was forgotten just a few months later.
  3. In 1996, I had two visitors to the group, Guillaume Cottenceau, a french undergraduate student, and Darek Bogdal, a Polish researcher who wanted to learn some HTML. Together they produced this, which was an interactive tutorial to accompany the NMR lecture notes previously mentioned. These pages introduce the Java applet (yes, it was very new in 1996), which Guillaume had written and which Darek then made use of. And hey, what do you know, the applet still works (although you might have to coerce your browser into accepting an unsigned applet).
  4. Here is a programming course that I had been running with Bryan Levitt for a few years, now recast into HTML web pages some time in 1994-5. This particular project I still hold dear, since it expanded upon the NMR lectures by getting the students to synthesize a FID (free induction decay) using the program they wrote, and then perform a Fourier Transform on it. I even encouraged students to present their results in HTML (I cannot now remember how many did). This link is to the computing facilities we offered students in 1994 for this project, ah those were the times! In 1996, the programming course was replaced by one on chemical information technologies, and here students were most certainly expected to write HTML. Some of the best examples are still available. And to illustrate how things happen in cycles, that course itself is now gone to be replaced by, yes, a programming course (but using Python, and not the original Fortran).
  5. In tracking down the materials for the programming course described above, I re-discovered something far older. It is linked here and is (some of) the Fortran source code I wrote as a PhD student in 1974 1972. So I will indulge in a short digression. My Ph.D. involved measuring rate constants, and the accepted method for analysing the raw kinetic data was using graph paper. For first order rate behaviour, this required one to measure a value at time=∞, which is supposed to be measured after ten half-lives. I was too impatient to wait that long, and worked out that a non-linear least squares analysis did not require the time=∞ value; indeed this value could be predicted accurately from the earlier measurements. So in 1974, I wrote this code to do this; no graph paper for me! Also for good measure is a least squares analysis of the Eyring equation. And you get proper standard deviations for your errors. In retrospect I should have commercialised this work, but in 1974, almost no-one paid money for software! What a change since then. I must try recompiling this code to see if it still works! And for good measure, here is a Huckel MO program I wrote in 1984 or earlier (I did compile this recently and found it works) and here is a little program for visualising atomic orbitals.
  6. In January 1994, I was asked to create a web page for the WATOC organisation. This certainly predated the web sites for e.g. the RSC, the ACS, indeed famous sites such as the BBC and Tesco (a large supermarket chain) which only started up in mid 1994. The WATOC site itself moved a few years ago.
  7. This is one of those wonderfully naive things I started in 1994, and which did not last long (in my hands). Nowadays, the concept lives on as MOOCs. Note again the almost complete expiry of the hyperlinks.
  8. This is a project we also started in 1994, Virtual reality[2],[3]. The idea was that if HTML was text-markup, VRML was going to be 3D markup. VRML itself never quite caught on, but it is having a new life as a 3D printing language!
  9. And by 1995, I felt confident enough in my ability to (edit) HTML, that we started a virtual conference in organic chemistry (we did four of them in the end). I remember the first one involved contributors sending me a Word version of their poster, and I did all the work in converting it into HTML. Such virtual conferences still run, but in truth most participants still prefer to travel long distances to go drink a beer with their chums, rather than hack HTML.

I am going to stop now, since this is far too much wallowing in the past. But at least all this stuff is not (yet) lost to posterity.

References

  1. H.S. Rzepa, B.J. Whitaker, and M.J. Winter, "Chemical applications of the World-Wide-Web system", Journal of the Chemical Society, Chemical Communications, pp. 1907, 1994. https://doi.org/10.1039/c39940001907
  2. O. Casher, and H.S. Rzepa, "Chemical collaboratories using World-Wide Web servers and EyeChem-based viewers", Journal of Molecular Graphics, vol. 13, pp. 268-270, 1995. https://doi.org/10.1016/0263-7855(95)00053-4
  3. O. Casher, C. Leach, C.S. Page, and H.S. Rzepa, "Advanced VRML based chemistry applications: a 3D molecular hyperglossary", Journal of Molecular Structure: THEOCHEM, vol. 368, pp. 49-55, 1996. https://doi.org/10.1016/s0166-1280(96)90535-7

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A two-publisher model for the scientific article: narrative+shared data.

Sunday, September 15th, 2013

I do go on rather a lot about enabling or hyper-activating[1] data. So do others[2]. Why is sharing data important?

  1. Reproducibility is a cornerstone in science,
  2. To achieve this, it is important that scientific research be open and transparent.
  3. Openly available research data is central to achieving this. It is estimated that less than 20% of the data collected in chemistry is made available in any open manner.
  4. RCUK (the UK research councils) wish increased transparency of publicly funded research and availability of its outputs‡

But it’s not all hot air, honestly. Peter Murray-Rust and I had started out on a journey to improve reproducibility, openness and transparency in (inter alia) scientific publishing in 1994. In 2001 we published an example of a data-rich article[3] based on CML, and by 2004 the concept had evolved into something Peter termed a datument[4]. Some forty such have now been crafted.[5]

In 2009, the journal Nature Chemistry was starting up, and I approached them with the idea of an interactive data exploratorium on the premise that a new journal might be receptive to new ways of presenting science. It was accepted and published[6] and was followed in 2010 by a second variation.[7] In both cases, these activated-figures were sent to the journal as part of the submission process, and hosted by them (they still are). You can even access them without a subscription to the journal!

Move on to 2012, when David Scheschkewitz had some very exciting silicon chemistry to report, we collaborated on some computational modelling, and sent the resulting article to Nature Chemistry for publication. This included the usual interactive table reporting the modelling and its data. However, it transpired that the production workflows for Nature Chemistry had been streamlined and I was informed that interactive tables could no longer be accepted. This time, we (i.e. the authors) would have to solve the issue of how to host and present the data ourselves.

I was very keen that this table be treated with equal weight to the article itself (citable in its own right) and that it not be downgraded to supporting information (ESI). My objection to ESI is that it is often poorly structured by authors, i.e. it is not prepared in a form which allows the data to be re-used, either by a perceptive human, or a logical machine. As a result it is often given little attention by referees (although bloggers seem to do a far better job) and furthermore can end up being lost behind a pay wall (the two Nature Chem interactive objects noted above can be openly accessed, but only if you know that they exist). So I determined that:

  1. The table should be immediately accessible by non-experts, but not through any convoluted processes of downloading a file, expanding it and finding the correct document within the resulting fileset to view in the correct program, which is how normal ESI is handled.
  2. The table and the data it contained within should be capable of acting as a scientific tool, forming what could be the starting point for a new investigation if appropriate.

To solve this issue, some lateral and quick thinking was needed. The solution was a two-component model in which the original article is treated as a “narrative“, intertwingled with a second, but nevertheless distinct component, the “data“. This data would follow the principles of the Amsterdam Manifesto; it would itself be citable. The two components would become symbiotes (a datument). The narrative[8] could cite this data and the data could back-link to the narrative. The data would inherit trust (i.e. peer review) from that applied to the narrative and the latter would inherit a date stamp and integrity from the data host (in this case Figshare[9]).*

The data itself can have two layers, presentation [9] using a combination of software (Jmol or JSmol for chemistry) which are used to invoke the “raw” data. That data itself is citable[10] (this is just a single example, resident as it happens on a different repository). The reader can choose use just the presentation layer or the underlying data.

The data object can be embedded in other pages; here it is below. The data sources for this table are themselves citable[11].

What are the advantages of such an approach? (the “what’s in it for me” question often asked by research students and their supervisors)

  1. Each of the components is held in an environment optimised for it and so can be presented to full advantage.
  2. The conventional narrative publisher does not necessarily also have to develop their own infrastructures for handling the data. They can choose to devolve that task to a “data publisher”.
  3. The data publisher (Figshare in this case) makes the data open. One does not need an institutional subscription to access it.
  4. “Added value” for each component can be done separately. Thus most narrative publishers would not necessarily wish to develop infrastructures for validating it or subsequently mining such “big data”. Indeed data mining of journals is prohibited by many publishers; it simply is either not possible or rendered so administratively difficult as to be impractical.
  5. Whilst a narrative article must clearly exist as a single instance (otherwise the authors would be accused of plagiarism), data can have multiple instances. Indeed, there exist protocols (SWORD) for moving data from one repository to another as the need arises. Publishing the same data in two or more locations is not currently considered plagiarism!
  6. The data component can be published as part of an article or say as part of a PhD thesis. This way, the creator of the data gets the advantages not of a date stamp associated with a narrative citation but of a much earlier stamp associated more closely with the actual creation of the data. That could easily and usefully resolve many disputes about who discovered what first, leaving the other issue of who interpreted what first to the narrative. I should mention that it is perfectly possible to “embargo” the data deposition so that it only becomes public when the narrative does (although you may choose not to do this).
  7. A data deposition cannot be modified, but a new version (which bidirectionally links back to the old one) can be published if say more data is collected at a future date.
  8. A whole infrastructure devoted just to enhancing the cited data can evolve; one that is unlikely to do so if the narrative publishers are the only stakeholders. For example, synthetic procedural data can be tagged using the excellent chemical tagger.
  9. It is relatively simple (=cheap) to build a pre-processor for publishing data, which for a research student can act as an electronic laboratory notebook, holding meta-data about the deposited/published data and the handles (doi) associated with each deposition. I have been using such an environment now for about seven years as the e-notebook for this blog for example. Thus the task of preparing figures and tables for a publication (or a blog post) is greatly facilitated. The same system is also used by research students and undergraduates for their lab work.
  10. I have noted previously how e.g. Google Scholar identifies data citations along with article citations in constructing an individual research profile. A researcher could become known for their published data as well as their published narratives. Indeed, it seems likely that the person who acquires and publishes the data, i.e. the research student, would then get accolades directly rather them all accruing to their supervisor.

But what can you, gentle reader of this blog, do to help? Well, ask if your institution already has, or plans to create a data repository. It can be local (we use DSpace) or “in-the-cloud” (e.g. Figshare). If not, ask why not! And if you are planning to submit an article for publication in the near future, ponder how you might better share its data.

‡ As first circulated on 28 April, 2011. See 
http://www.epsrc.ac.uk/about/standards/researchdata/Pages/policyframework.aspx

†The example given at the start of this post[8] contains only one table processed in this manner; the actual synthetic procedures are still held in more conventional SI.

*This blog uses the excellent Kcite plugin to manage citations.

The good folks at Figshare were extremely helpful in converting this deposition into an interactive presentation. Thanks guys!

References

  1. O. Casher, G.K. Chandramohan, M.J. Hargreaves, C. Leach, P. Murray-Rust, H.S. Rzepa, R. Sayle, and B.J. Whitaker, "Hyperactive molecules and the World-Wide-Web information system", Journal of the Chemical Society, Perkin Transactions 2, pp. 7, 1995. https://doi.org/10.1039/p29950000007
  2. R. Van Noorden, "Data-sharing: Everything on display", Nature, vol. 500, pp. 243-245, 2013. https://doi.org/10.1038/nj7461-243a
  3. P. Murray-Rust, H.S. Rzepa, and M. Wright, "Development of chemical markup language (CML) as a system for handling complex chemical content", New Journal of Chemistry, vol. 25, pp. 618-634, 2001. https://doi.org/10.1039/b008780g
  4. H.S. Rzepa, "Chemical datuments as scientific enablers", Journal of Cheminformatics, vol. 5, 2013. https://doi.org/10.1186/1758-2946-5-6
  5. H.S. Rzepa, "Transclusions of data into articles", 2013. https://doi.org/10.6084/m9.figshare.797481
  6. H.S. Rzepa, "The importance of being bonded", Nature Chemistry, vol. 1, pp. 510-512, 2009. https://doi.org/10.1038/nchem.373
  7. H.S. Rzepa, "The rational design of helium bonds", Nature Chemistry, vol. 2, pp. 390-393, 2010. https://doi.org/10.1038/nchem.596
  8. M.J. Cowley, V. Huch, H.S. Rzepa, and D. Scheschkewitz, "Equilibrium between a cyclotrisilene and an isolable base adduct of a disilenyl silylene", Nature Chemistry, vol. 5, pp. 876-879, 2013. https://doi.org/10.1038/nchem.1751
  9. D. Scheschkewitz, M.J. Cowley, V. Huch, and H.S. Rzepa, "The Vinylcarbene – Cyclopropene Equilibrium of Silicon: an Isolable Disilenyl Silylene", 2013. https://doi.org/10.6084/m9.figshare.744825
  10. H.S. Rzepa, "Gaussian Job Archive for C60H92Si3", 2012. https://doi.org/10.6084/m9.figshare.96410

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Research data and the “h-index”.

Monday, June 24th, 2013

The blog post by Rich Apodaca entitled “The Horrifying Future of Scientific Communication” is very thought provoking and well worth reading. He takes us through disruptive innovation, and how it might impact upon how scientists communicate their knowledge. One solution floated for us to ponder is that “supporting Information, combined with data mining tools, could eliminate most of the need for manuscripts in the first place“. I am going to juxtapose that suggestion on something else I recently discovered. 

Someone encouraged me to take a look at Google Scholar. It is one of those resources that, amongst other features, computes an individual’s h-index and i10-index (the former, having gone through its purple patch, is now apparently at the end of the road, at least for chemists). One reason perhaps why proper curation of research data is not high on most chemists’ list of priorities is that it does not contribute to one’s h-index, and particularly one’s prospects of a successful research career. Thus “supporting information (data)” is one of those things, like styling the citations in a research article, that most people probably prepare through gritted teeth (a rather annoying ritual without which a research article cannot be published). So when I inspected my own Google Scholar profile (you can do the same here) I was rather surprised to find, appended to all the regular research articles, a long list of data citations (sic!). Because I have placed much of my own data into a digital repository,‡ this has opened it up to Google (where don’t they get to nowadays?) for listing (if not actually mining). These citations of themselves actually do not (currently?) contribute to eg the h-index, since currently these entries are not attracting citations by others. And that of course is because doing so is not yet an accepted part of the ritual of preparing a scientific article.

Most scientists must now be pondering what the future holds in terms of how they can bring themselves to the attention of others (in a good way) and hence progress their careers. So I will take Rich’s suggestion one step further. Those scientists who create new data in a process called research, should firstly curate this data properly (via eg a digital repository) and then expect to promote their activity by garnering not only citations for the published narratives (= articles) but also associated published data. Their success as a researcher would be (in part) judged by both. Who knows, as well as famous published narratives, perhaps we will also rank famous published datasets! 

‡I do the same for the data I use to support many of the posts for this blog.

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