I hereby want to share an interesting way to get access to an extensive data set in the field of Altmetrics. Several well developed Altmetric data providers collect a variety of metrics, thereby covering a large part of social attention that papers receive. One of these platforms is Altmetric.com.

Info@altmetric.com

In order to get access to the data send an e-mail to the address mentioned above. Tell them you are interested in using the data and they will send you the ‘Altmetric Data Researcher Access Application Form’. After filling this out, you will get access to the data within a couple days. You could also look on their page and download the request form, I think this is the correct one, see below:

https://www.altmetric.com/audience/researchers/

The founder of the platform is Stacy Konkiel, who is always willing to help and a very pleasant woman. For any questions related to the data I think you should contact her.

Good luck on your thesis!

Mark Stoevelaar

This letter is part of the collection “letter to the younger self” and has been written for helping the “new generation of students” learning from who was there before. You can see all the letters at the following link:

https://francescolelli.info/category/letter-to-the-younger-self/

Other post related to altermetrics include the following:

• https://francescolelli.info/generic/scientometrics-and-scientific-citations-in-patent-a-database/
• https://francescolelli.info/thesis/promotion-of-scientific-work-thesis-proposal/

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Scientometrics is a way to measure how science, technology, and economy are intrinsically connected with each other. Understanding how undeniably constitutes valuable economic knowledge. However, in order to understand the connections between those phenomena a suitable methodological environment needs to be created.

The following example represents a classical use case of investigation
Scientometrics

PATSTAT is a product of the European Patent Office (EPO, https://www.epo.org). It is a periodical snapshot of patent related information organized in a relational database model. Records on patent applications, their applicants and publications are available. A table with a code name “tls214_npl_publn” (often referred to as TLS214) stores information on bibliographic references like the one shown in Figure 1. This table contains more than 30 million records. The records, however, are often duplicated or inaccurate. Moreover, a full bibliographic reference is stored in only one attribute. This makes it problematic to query the table for relevant information, for example, to retrieve an author’s name or the date of a specific publication.

Disambiguation in the context of data management refers to the identification of unique entities within a dataset. Such entities are identified by a unique identifier that can be assigned to many database records, which effectively describe the same bibliographic entity. The problem of data duplication and ambiguity arises due to (among other reasons):

1. Lack of consistent input (transcription) convention;
2. Variable level of input (detail) accuracy;
3. Missing data;
4. Different order of transcription of the same information;
5. Typos.

The following table excerpt illustrates the problem:

All of the records shown above refer to the same entity – the paper by E.F. Codd on the relational database model (Codd, 1970). However, the references to the same entity are given in different ways or are simply duplicated. For example, record 7 does not contain Codd’s name initial or month information, while record 8 contains full transcription of the abbreviated name “ACM” at the end of the string. All the records describe the same bibliographic entity but are treated as distinct entities by the primary key of the TLS214 relation – the “npl_publn_id” attribute.

Such a design makes it very difficult to use information in the table in a correct way. For example, say a researcher is interested in the relation between science and technology. She assesses that a scientific discovery is well proxied by a publication of a scientific paper, while a piece of technology can be modelled as a patent publication. However, due to the unsupervised procedure in which citations are added to the PATSTAT database it is difficult for her to specify a query that takes into account all the possible variation in records that describe the same bibliographic entity. As a result, the researcher is unable to properly count all of the scientific references to the same bibliographic entity. This results in incorrect patent statistics. As much as it may be possible to identify a single researcher and his body of work (like E.F. Codd), studies on a population of researchers are impossible without a prior cleansing and de-duplication of the PATSTAT database.

Scientometrics: Problem of disambiguating scientific references

The objective of an automated method for cleaning is the disambiguation of all scientific references in the original table of the PATSTAT database proposing techniques for advancing the
Scientometrics research field. “Scientific references” refer to the types of records in the table that describe entities that can be classified as publications. Notice that not all records in the table are scientific references, but can also be references to other patents. The final result of the procedure is a table with clusters of name variants (records) for each, unique scientific entity. The next table presents an example cluster with label 231 for the paper by E.F. Codd.

This introduction should serve as framework for understanding the following thesis proposal in the area of scientometrics: Human Enhanced Machine Driven-Categorization

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We are looking for students that want to explore a research theme in the area of scientometrics, which is the study of measuring and analyzing science, technology and innovation. In particular, we are looking for ‘self-starter’ students that are capable to challenge the status quo and providing innovative and out of the box ideas for this research theme. He/she will explore this research theme and will bring an information management perspective to this area. In short, he will study how social media (Facebook, Twitter, etc.) can influence the promotion, marketing, and dissemination of scholarly articles and the development of research indicators based on social media activity (see the references for background information).
This project does not involve an internship. Instead, it will try to have a high academic relevance and theoretical contribution and, based on the quality of your work the candidate may be able to publish the results in the proceedings and scientific journals.
We currently envisage two theses to be written related to the research theme, one that will analyze the theoretical aspect of the domain and one more tool oriented that is ideal for candidates that prefer a more hands on approach:

1. A overview and review of the current literature on the promotion of scientific work on the author level, journal level, and other dimensions. The literature might be captured in the form of a bibliometric network in VOSviewer (see vosviewer.com). VOSviewer is a software tool for constructing and visualizing bibliometric networks. In addition, want to understand the variables and weight of the variables that make up the marketing of scientific papers. For this thesis programming skills are not essential.
2. Design-oriented thesis that will lead to the design of a prototype for measuring the social impact of the scientific work. The candidate will have the opportunity to learn how modern eCommerce and eMarketing solutions works in real life and will design a framework for quantify how socially driven scientometric indicators influence the impact of the research in society and in research communities.

A few references related to the promotion of scientific work:

• Sugimoto, C. R., Work, S., Larivière, V. and Haustein, S. (2017), Scholarly use of social media and altmetrics: A review of the literature. Journal of the Association for Information Science and Technology, 68: 2037-2062. doi:10.1002/asi.23833.
• Wang Xianwen, Yunxue Cui, Qingchun Li, and Xinhui Guo. (2017), Social Media Attention Increases Article Visits: An Investigation on Article-Level Referral Data of PeerJ, Frontiers in Research Metrics and Analytics
  2 (December 20, 2017). doi:10.3389/frma.2017.00011.
• Hadas Shema, Judit Bar‐Ilan and Mike Thelwall. (2015), Do blog citations correlate with a higher number of future citations? Research blogs as a potential source for alternative metrics. https://doi.org/10.1002/asi.23037

If you are interested in the topic, do not hesitate to contact us for more information. Our contact details are: Emiel Caron & Francesco Lelli.

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