Posts Tagged ‘Elsevier’
Friday, April 12th, 2019
In recent years, findable data has become ever more important (the F in FAIR). Here I test that F using the DataCite search service.
Firstly an introduction to this service. This is a metadata database about datasets and other research objects. One of the properties is relatedIdentifier which records other identifiers associated with the dataset, being say the DOI of any published article associated with the data, but it could also be pointers to related datasets.
One can query thus:
- https://search.datacite.org/works?query=relatedIdentifiers.relatedIdentifier:*
which retrieves the very healthy looking 6,179,287 works.
- One can restrict this to a specific publisher by the DOI prefix assigned to that publisher:
?query=relatedIdentifiers.relatedIdentifier:10.1021*
which returns a respectable 210,240 works.
- It turns out that the major contributor to FAIR currently are crystal structures from the CCDC. One can remove them from the search to see what is left over:
?query=(relatedIdentifiers.relatedIdentifier:10.1021*)+NOT+(identifier:*10.5517*)
and one is down to 14,213 works, of which many nevertheless still appear to be crystal structures. These may be links to other crystal datasets.
I have performed searches 2 and 3 for some popular publishers of chemistry (the same set that were analysed here).
| Publisher |
Search 2 |
Search 3 |
| ACS |
210,240 |
14,213 |
| RSC |
138,147 |
1,279 |
| Elsevier |
185,351 |
56,373 |
| Nature |
12,316 |
8,104 |
| Wiley |
135,874 |
9,283 |
| Science |
3,384 |
2,343 |
These publishers all have significant numbers of datasets which at least accord with the F of FAIR. A lot of data sets may not have metadata which in fact points back to a published article, since this can be something that has to be done only when the DOI of that article appears, in other words AFTER the publication of the dataset. So these numbers are probably low rather than high.
How about the other way around? Rather than datasets that have a journal article as a related identifier, we could search for articles that have a dataset as a related identifier?
- ?query=(identifier:*10.1039*)+AND+(relatedIdentifiers.relatedIdentifier:*)
returns rather mysterious nothing found. It might also be that there is no mapping of this search between the CrossRef and DataCite metadata schemas.
- And just to show the searches are behaving as expected:
?query=(relatedIdentifiers.relatedIdentifier:10.1021*)+AND+(identifier:*10.5517*)
returns 196,027 works.
It will also be of interest to show how these numbers change over time. Is there an exponential increase? We shall see.
Finally, we have not really explored adherence to eg the AIR of FAIR. That is for another post.
Tags:Academic publishing, DataCite, Digital Object Identifier, Digital technology, Elsevier, Findability, Identifiers, Information, Information architecture, Information science, Knowledge, Knowledge representation, search service, Web design
Posted in Chemical IT | 1 Comment »
Saturday, February 16th, 2019
The title of this post comes from the site www.crossref.org/members/prep/ Here you can explore how your favourite publisher of scientific articles exposes metadata for their journal.
Firstly, a reminder that when an article is published, the publisher collects information about the article (the “metadata”) and registers this information with CrossRef in exchange for a DOI. This metadata in turn is used to power e.g. a search engine which allows “rich” or “deep” searching of the articles to be undertaken. There is also what is called an API (Application Programmer Interface) which allows services to be built offering deeper insights into what are referred to as scientific objects. One such service is “Event Data“, which attempts to create links between various research objects such as publications, citations, data and even commentaries in social media. A live feed can be seen here.
So here are the results for the metadata provided by six publishers familiar to most chemists, with categories including;
- References
- Open References
- ORCID IDs
- Text mining URLs
- Abstracts
RSC
ACS
Elsevier
Springer-Nature
Wiley
Science
One immediately notices the large differences between publishers. Thus most have 0% metadata for the article abstracts, but one (the RSC) has 87%! Another striking difference is those that support open references (OpenCitations). The RSC and Springer Nature are 99-100% compliant whilst the ACS is 0%. Yet another variation is the adoption of the ORCID (Open Researcher and Collaborator Identifier), where the learned society publishers (RSC, ACS) achieve > 80%, but the commercial publishers are in the lower range of 20-49%.
To me the most intriguing was the Text mining URLs. From the help pages, “The Crossref REST API can be used by researchers to locate the full text of content across publisher sites. Publishers register these URLs – often including multiple links for different formats such as PDF or XML – and researchers can request them programatically“. Here the RSC is at 0%, ACS is at 8% but the commercial publishers are 80+%. I tried to find out more at e.g. https://www.springernature.com/gp/researchers/text-and-data-mining but the site was down when I tried. This can be quite a controversial area. Sometimes the publisher exerts strict control over how the text mining can be carried out and how any results can be disseminated. Aaron Swartz famously fell foul of this.
I am intrigued as to how, as a reader with no particular pre-assembled toolkit for text mining, I can use this metadata provided by the publishers to enhance my science. After all, 80+% of articles with some of the publishers apparently have a mining URL that I could use programmatically. If anyone reading this can send some examples of the process, I would be very grateful.
Finally I note the absence of any metadata in the above categories relating to FAIR data. Such data also has the potential for programmatic procedures to retrieve and re-use it (some examples are available here[1]), but apparently publishers do not (yet) collect metadata relating to FAIR. Hopefully they soon will.
References
- A. Barba, S. Dominguez, C. Cobas, D.P. Martinsen, C. Romain, H.S. Rzepa, and F. Seoane, "Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data", ACS Omega, vol. 4, pp. 3280-3286, 2019. https://doi.org/10.1021/acsomega.8b03005
Tags:Aaron Swartz, Academic publishing, API, Business intelligence, CrossRef, data, Data management, Elsevier, favourite publisher, Identifiers, Information, Information science, Knowledge, Knowledge representation, metadata, mining, ORCiD, PDF, Pre-exposure prophylaxis, Publishing, Publishing Requirements for Industry Standard Metadata, Records management, Research Object, Scholarly communication, Scientific literature, search engine, social media, Technical communication, Technology/Internet, text mining, Written communication, XML
Posted in Interesting chemistry | 1 Comment »
Saturday, February 16th, 2019
The title of this post comes from the site www.crossref.org/members/prep/ Here you can explore how your favourite publisher of scientific articles exposes metadata for their journal.
Firstly, a reminder that when an article is published, the publisher collects information about the article (the “metadata”) and registers this information with CrossRef in exchange for a DOI. This metadata in turn is used to power e.g. a search engine which allows “rich” or “deep” searching of the articles to be undertaken. There is also what is called an API (Application Programmer Interface) which allows services to be built offering deeper insights into what are referred to as scientific objects. One such service is “Event Data“, which attempts to create links between various research objects such as publications, citations, data and even commentaries in social media. A live feed can be seen here.
So here are the results for the metadata provided by six publishers familiar to most chemists, with categories including;
- References
- Open References
- ORCID IDs
- Text mining URLs
- Abstracts
RSC
ACS
Elsevier
Springer-Nature
Wiley
Science
One immediately notices the large differences between publishers. Thus most have 0% metadata for the article abstracts, but one (the RSC) has 87%! Another striking difference is those that support open references (OpenCitations). The RSC and Springer Nature are 99-100% compliant whilst the ACS is 0%. Yet another variation is the adoption of the ORCID (Open Researcher and Collaborator Identifier), where the learned society publishers (RSC, ACS) achieve > 80%, but the commercial publishers are in the lower range of 20-49%.
To me the most intriguing was the Text mining URLs. From the help pages, “The Crossref REST API can be used by researchers to locate the full text of content across publisher sites. Publishers register these URLs – often including multiple links for different formats such as PDF or XML – and researchers can request them programatically“. Here the RSC is at 0%, ACS is at 8% but the commercial publishers are 80+%. I tried to find out more at e.g. https://www.springernature.com/gp/researchers/text-and-data-mining but the site was down when I tried. This can be quite a controversial area. Sometimes the publisher exerts strict control over how the text mining can be carried out and how any results can be disseminated. Aaron Swartz famously fell foul of this.
I am intrigued as to how, as a reader with no particular pre-assembled toolkit for text mining, I can use this metadata provided by the publishers to enhance my science. After all, 80+% of articles with some of the publishers apparently have a mining URL that I could use programmatically. If anyone reading this can send some examples of the process, I would be very grateful.
Finally I note the absence of any metadata in the above categories relating to FAIR data. Such data also has the potential for programmatic procedures to retrieve and re-use it (some examples are available here[1]), but apparently publishers do not (yet) collect metadata relating to FAIR. Hopefully they soon will.
References
- A. Barba, S. Dominguez, C. Cobas, D.P. Martinsen, C. Romain, H.S. Rzepa, and F. Seoane, "Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data", ACS Omega, vol. 4, pp. 3280-3286, 2019. https://doi.org/10.1021/acsomega.8b03005
Tags:Aaron Swartz, Academic publishing, API, Business intelligence, CrossRef, data, Data management, Elsevier, favourite publisher, Identifiers, Information, Information science, Knowledge, Knowledge representation, metadata, mining, ORCiD, PDF, Pre-exposure prophylaxis, Publishing, Publishing Requirements for Industry Standard Metadata, Records management, Research Object, Scholarly communication, Scientific literature, search engine, social media, Technical communication, Technology/Internet, text mining, Written communication, XML
Posted in Interesting chemistry | 1 Comment »
