Archive for the ‘Web Science’ Category

Do we have a magic flute for K-12 Web Science?

October 27th, 2015

In early July of 2015, Tetherless World Constellation (TWC) opened its door for four young men of the 2015 summer program of Rensselaer Research Experience for High School Students. The program covered a period of four weeks and each student was asked to choose a small and focused topic for research experience. They each were also asked to prepared a poster and present it in public at the end of the program.

Web Science was the discipline chosen by the four high school students at TWC. Before their arrival several professors, research scientists and graduate students formed a mentoring group, and officially I was assigned the task to mentor two of the four students. Such a fresh experience! And then a question came up was: do we have a curriculum of Web Science for High School Students? And for a period of four weeks? We do have excellent textbooks for Semantic Web, Data Science, and more, but most of them are not for high school students. Also the ‘research centric’ feature of the summer program indicated that we should not focus only on teaching but perhaps needed to spend more time on advising a small research project.

My simple plan was, for week 1 we focused on basic concepts, for weeks 2 and 3 the students were assigned a specific topic taken from an existing project, and for week 4 we focused on result analysis, wrap up and poster preparation. A google doc was used to record the basic concepts, technical resources and assignments we introduced and discussed in week 1. I thought those materials could be a little bit more for the students, but to my surprise they took them up really fast, which gave me the confidence to assign them research topics from ongoing projects. One of the students was asked to do statistical analysis of records on the Deep Carbon Observatory Data Portal, and presented the results in interactive visualizations. The other student worked on the visualization of geologic time and connections to Web resources such as Wikipedia. Technologies used were RDF database, SPARQL query, JavaScript, D3.js and JSON data format.

Hope the short program has evoked the students’ interest to explore more and deeper in Web Science. Some of them will soon graduate from high school and go to universities. Wish them good luck!

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Open Science in an Open World

December 21st, 2014

I began to think about a blog for this topic after I read a few papers about Open Codes and Open Data published in Nature and Nature Geoscience in November 2014. Later on I also noticed that the editorial office of Nature Geoscience made a cluster of articles themed on Transparency in Science (, which really created an excellent context for further discussion of Open Science.

A few weeks later I attended the American Geophysical Union (AGU) Fall Meeting at San Francisco, CA. That is used to be a giant meeting with more than 20,000 attendees. My personal focus is presentations, workshops and social activities in the group of Earth and Space Science Informatics. To summarize the seven-day meeting experience with a few keywords, I would choose: Data Rescue, Open Access, Gap between Geo and Info, Semantics, Community of Practice, Bottom-up, and Linking. Putting my AGU meeting experience together with thoughts after reading the Nature and Nature Geoscience papers, now it is time for me to finish a blog.

Besides incentives for data sharing and open source policies of scholarly journals, we can extend the discussion of software and data publication, reuse, citation and attribution by shedding more light on both technological and social aspects of an environment for open science.

Open science can be considered as a socio-technical system. One part of the system is a way to track where everything goes and another is a design of appropriate incentives. The emerging technological infrastructure for data publication adopts an approach analogous to paper publication and has been facilitated by community standards for dataset description and exchange, such as DataCite (, Open Archives Initiative-Object Reuse and Exchange ( and the Data Catalog Vocabulary ( Software publication, in a simple way, may use a similar approach, which calls for community efforts on standards for code curation, description and exchange, such as the Working towards Sustainable Software for Science ( Simply minting Digital Object Identifiers to codes in a repository makes software publication no difference from data publication (See also: . Attention is required for code quality, metadata, license, version and derivation, as well as metrics to evaluate the value and/or impact of a software publication.

Metrics underpin the design of incentives for open science. An extended set of metrics – called altmetrics – was developed for evaluating research impact and has already been adopted by leading publishers such as Nature Publishing Group ( Factors counted in altmetrics include how many times a publication has been viewed, discussed, saved and cited. It was very interesting to read some news about funders’ attention to altmetrics ( on my flight back from the AGU meeting – from the 12/11/2014 issue of Nature which I picked from the NPG booth at the AGU meeting exhibition hall. For a software publication the metrics might also count how often the code is run, the use of code fragments, and derivations from the code. A software citation indexing service – similar to the Data Citation Index ( of Thomson Reuters – can be developed to track citations among software, datasets and literature and to facilitate software search and access.

Open science would help everyone – including the authors – but it can be laborious and boring to give all the fiddly details. Fortunately fiddly details are what computers are good at. Advances in technology are enabling the categorization, identification and annotation of various entities, processes and agents in research as well as the linking and tracing among them. In our 06/2014 Nature Climate Change article we discussed the issue of provenance of global change research ( Those works on provenance capture and tracing further extend the scope of metrics development. Yet, incorporating those metrics in incentive design requires the science community to find an appropriate way to use them in research assessment. A recent progress is that NSF renamed Publications section as Products in the biographical sketch of funding applicants and allowed datasets and software to be listed ( To fully establish the technological infrastructure and incentive metrics for open science, more community efforts are still needed.

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Data Management – Serendipity in Academic Career

November 11th, 2014

A few days ago I began to think about the topic for a blog and the first reflection in my mind was ‘data management’ and then a Chinese poem sentence ‘无心插柳柳成荫’ followed. I went to Google for an English translation of that sentence and the result was ‘Serendipitiously’. Interesting, I never saw that word before and I had to use a dictionary to find that ‘serendipity’ means unintentional positive outcomes, which expresses the meaning of that Chinese sentence quite well. So, I regard data management as serendipity in my academic career. I think that’s because I was trained as a geoinformatics researcher through my education in China and the Netherlands, how it comes that most of my current time is being spent on data management?

One clue I could see is that I have been working on ontologies, vocabularies and conceptual models for geoscience data services, which is relevant to data management. Another more relevant clue is a symposium ‘Data Management in Research: A Challenging Issue’ organized at University of Twente campus in 2011 spring. Dr. David Rossiter, Ms. Marga Koelen, I and a few other ITC colleagues attend the event. That symposium highlighted both technical and social/cultural issues faced by the 3TU.Datacentrum (, a data repository for the three technological universities in the Netherlands. It is very interesting to see that several topics of my current work had already discussed in that symposium, whereas I paid almost no attention because I was completely focused on my vocabulary work at that time. Since now I am working on data management, I would like to introduce a few concepts relevant to it and the current social and technical trends.

Data management, in simple words, means what you will do with your datasets during and after a research. Conventionally, we treat paper as the ‘first class’ product of research and many scientists pay less attention to data management. This may lower the efficiency of research activities and hinder communications among research groups in different institutions. There is even a rumor that 80% of a scientist’s time is spent on data discovery, retrieval and assimilation, and only 20% of time is for data analysis and scientific discovery. An ideal situation is that reverse the allocation of time, but that requires efforts on both a technical infrastructure for data publication and a set of appropriate incentives to the data authors.

After coming to United States the first data repository caused my attention was the California Digital Library (CDL) (, which is similar to the services offered by 3TU.Datacentrum. I like the technical architecture CDL work not only because they provide a place for depositing datasets but also, and more importantly, they provide a series of tools and services ( to allow users to draft data manage plans to address funding agency requirements, to mint unique and persistent identifiers to published datasets, and to improve the visibility of the published datasets. The word data publication is derived from paper publication. By documenting metadata, minting unique identifiers (e.g., Digital Object Identifiers (DOIs)), and archiving copies of datasets into a repository, we can make a piece of published dataset similar to a piece of published paper. The identifier and metadata make the dataset citable, just like what we do with published papers. A global initiative, the DataCite, had been working on standards of metadata schema and identifier for datasets, and is increasing endorsed by data repositories across the word, including both CDL and 3TU.Datacentrum. A technological infrastructure for data publication is emerging, and now people begin to talk about the cultural change to treat data as ‘first class’ product of research.

Though funding agencies already require data management plans in funding proposals, such as the requirements of National Science Foundation in US and the Horizon 2020 in EU (A Google search with key word ‘data management’ and the name of the funding agency will help find the agency’s guidelines), The science community still has a long way to go to give data publication the same attention as what they do with paper publication. Various community efforts have been take to promote data publication and citation. The FORCE11 published the Joint Declaration of Data Citation Principles ( in 2013 to promote good research practice of citing datasets. Earlier than that, in 2012, the Federation of Earth Science Information Partners published Data Citation Guidelines for Data Providers and Archives (, which offers more practical details on how a piece of published dataset should be cited. In 2013, the Research Data Alliance ( was launched to build the social and technical bridges that enable open sharing of data, which enhances existing efforts, such as CODATA (, to promote data management and sharing.


To promote data citation, a number of publishers have launched so called data journals in recent years, such as Scientific Data ( of Nature Publishing Group, Geoscience Data Journal ( of Wiley, and Data in Brief ( of Elsevier. Such a data journal often has a number of affiliated and certified data repositories. A data paper allows the authors to describe a piece of dataset published in a repository. A data paper itself is a journal paper, so it is citable, and the dataset is also citable because there are associated metadata and identifier in the data repository. This makes data citation flexible (and perhaps confusing): you can cite a dataset by either citing the identifier of the associated data paper, or the identifier of the dataset itself, or both. More interestingly, a paper can cite a dataset, a dataset can cite a dataset, and a dataset can also cite paper (e.g., because the dataset may be derived from tables in a paper). The Data Citation Index ( launched by Thomson Reuters provides services to index the world’s leading data repositories, connect datasets to related literature indexed in the Web of Science database and to search and access data across subjects and regions.

Although there is such huge progress on data publication and citation, we are not yet there to fully treat data as ‘first class’ products of research. A recent good news is that, in 2013, the National Science Foundation renamed Publications section in biographical sketch of funding applicants as Products and allowed datasets and software to be listed there ( However, this is still just a small step. We hope more similar incentives appear in academia. For instance, even we have the Data Citation Index, are we ready to mix the data citation and paper citation to generate the H-index of a scientist? And even there is such an H-index, are we ready to use it in research assessment?

Data management involves so many social and technological issues, which make it quite different from pure technical questions in geoinformatics research. This is an enjoyable work and in the next step I may spend more time on data analysis, for which I may introduce a few ideas in another blog.

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Open Source Software & Science Reproducibility

January 14th, 2014

This year my contribution to the AGU fall meeting 2013 was all about the development of Open Source Software to enable the reproducibility of scientific products, with both a Poster and an Oral presentation. The AGU was the perfect opportunity to share my ideas on a topic that is one of my main interests.

This was my 2nd time at AGU, but my first time with an oral presentation which turned in a real challenge!

The main issue was a combination of 2 factors : I had decided to generate the slideshow in realtime as HTML from an online IPython Notebook. I thought it would be cool to show this functionality, as well as the work itself. Unfortunately, I was dependent on an internet connection at the time of the presentation, but alas, at AGU the presenter computer doesn’t have internet connection! Definitely not the best conditions for a web based slideshow generated “on-the-fly” by the execution of an IPython Notebook.

I found out about the lack of connectivity only 2 days before my presentation. I must have misunderstood the AGU oral presentation guidelines, but when I didn’t find an explicit mention of the lack of an internet connection, I took it for granted that that wouldn’t be an issue. Big mistake!

I decided it would be safer to prepare a power-point presentation, and some time later, I had one. Deep breath; I would be safe. But… what a disappointment !

I was so excited about the idea of showing my work running in realtime instead of showing a static (somewhat boring) ppt  presentation!!!

I kept thinking about alternative solutions, though, and an idea quickly came to me. If the lack of internet stands in the way of an interactive, realtime demo there should be no problem in running a static HTML slideshows instead; at least that is what I thought …

I used the IPython “nbconvert” utility and its “convert to slide” option, and I successfully converted my workflow from an interactive IPython notebook running in slideshow mode to a static HTML5 slideshows, yeah! The audience wouldn’t get to see how this was done, but at least they would get to see the result.

Happy with the final HTML presentation I finally went to the “AGU’s Speaker Ready Room” to upload and test my presentation. Unfortunately, my HTML presentation would not run offline. The lack of internet was giving me troubles with missing JavaScript files, missing fonts, images-urls to be replaced with path to static files, broken hyperlinks etc … it was not as easy as I thought.

It took more than 3 hours to fix all the bugs on account of a really slow internet connection running from my phone, but finally i got my presentation perfectly  running off line on the AGU computers !

In the end, my talk ran very smoothly. A complete workflow for “catchments characterization” using exclusively open source software, running online and fully reproducible thanks to the use of open source software and an open dataset! I felt really good, as I think I successfully got my message across, both in words and in actions.

To top it all off, my presentation came just at the right time. Before me, two other presentations during my session had mentioned the use of the IPython Notebook as open source software tool to enable reproducibility of scientific work. They had highlighted that it shows great potential and that it deserves further investigation. I think my presentation gave them even more proof of that! Even the chairman acknowledged this when he stated: “Before we heard about it, but now we saw it in action!” I felt very proud of what I had done. The effort I put into running the HTML slideshow definitely paid off!!!


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What is ontology?

December 19th, 2013

The topic of a blog in my mind, after five days at the American Geophysical Union 2013 Fall Meeting discussing Earth and space science informatics, is to give an introduction of ontology to researchers in Earth and environmental sciences and beyond.

To attract your interest, I would say that ontology is the invisible hand behind anything. (It took me a few minutes to think about whether I should add an ‘an’ before the ‘ontology’ here. For reasons see below.)

First let’s see the etymology of the word ‘ontology’. According to Wiktionary (, ontology is ‘originally Latin ontologia (1606, Ogdoas Scholastica, by Jacob Lorhard (Lorhardus)), from Ancient Greek ὤν (ōn, “on”), present participle of εἰμί (eimi, “being, existing, essence”) + λόγος (logos, “account”).’

Second let’s see the definition of the word. It is also interesting to see that Wiktionary claims that in philosophy the word ‘ontology’ can be either uncountable or countable. For the former, ontology is defined by Wiktionary as ‘The branch of metaphysics that addresses the nature or essential characteristics of being and of things that exist; the study of being.’ This definition is more or less the same as another one done by the Oxford English Dictionary, ‘The science or study of being; that branch of metaphysics concerned with the nature or essence of being or existence.’ That Oxford definition was used in my PhD defense ( For the countable ‘ontology’, Wiktionary defines it as ‘The theory of a particular philosopher or school of thought concerning the fundamental types of entity in the universe.’ I had not done any work relevant to that definition yet but I just found Oxford also has a similar definition ‘As a count noun: a theory or conception relating to the nature of being.’

The word metaphysics is mentioned in the definition of ontology as an unaccountable noun. In now days when people talk about metaphysics they often refer to Aristotle (384 – 322 BCE). If you (especially those who are working for a Doctor of PHILOSOPHY ;-)) are interested in his study you can read the two most famous books 1) Politics: A Treatise on Government and 2) The Ethics of Aristotle by him on the Gutenberg website ( The story does not stop here. In a famous Chinese book, I Ching (or the Book of Changes, c. 450 – 250 BCE), there are also topics about metaphysics, such as a sentence which is my personal favorite: ‘What is above form is called Tao; what is within form is called tool.’

The philosophical meaning of the word ontology is the background and for most cases in the domain of Earth and space science informatics we care more about another meaning of the word: ontology as a countable noun in computer science. Before discussing definition of ontology as a computer science word, let’s first see how hot this word is in recent years. I did a few searches with the topic ‘ontology’ in (on Dec 19, 2013), which showed that there are about 44884 publications for all years, and publication numbers for separate periods are 1470/1945–1995, 1498/1995–2000, ~7901/2000–2005, ~24528/2005–2010, and ~16891/2010–2013. If I refined the results by limiting to the research area ‘Computer Science’, the results are: ~22251/all years, 114/1945–1995, 673/1995–2000, ~5095/2000–2005, ~14316/2005–2010, and ~5971/2010–2013. And there are a big number of publications that applied informatics and were filtered out by the keyword ‘Computer Science’. From those results we can see many meanings, one is that works with the computer science ‘ontology’ has been increasing significantly since 2000.

For the definition of the computer science word ‘ontology’, many people have cited the publications of T.R. Gruber (1993, 1995, see: and ‘An ontology is an explicit specification of a conceptualization’. Middle 1990s is the golden age for discussing the definition of ontology. N. Guarino (1997, see: made a nice review of the definition of ‘ontology’, in which I think one key point he discussed was the ‘shared conceptualization’ feature of an ontology. So in my PhD dissertation (Ma, 2011, see: I tried to re-address the definition of the computer science ‘ontology’: ‘Ontologies in computer science are defined as shared conceptualizations of domain knowledge (Gruber, 1995; Guarino, 1997b)…’

Third, after seeing the definition of ontology, let’s focus on how to put a computer science ‘ontology’ into practice, especially in the domain of Earth and space science informatics. Early 2000s is the golden age for that work. McGuinness (2003, see: made a wonderful discussion of the ontology spectrum. McGuinness also made a footnote to that spectrum figure: ‘This spectrum arose out of a conversation in preparation for an ontology panel at AAAI ’99. The panelists (Gruninger, Lehman, McGuinness, Ushold, and Welty), chosen because of their years of experience in ontologies found that they encountered many forms of specifications that different people termed ontologies. McGuinness refined the picture to the one included here.’ When I was doing my PhD I read this note and I tried to find a few other publications by people in the panelists listed by McGuinness, and I did find a few that also discussed the ontology spectrum, for example:
Welty, C., 2002. Ontology-driven conceptual modeling. In: Pidduck, A.B., Mylopoulos, J., Woo, C.C., Ozsu, M.T. (Eds.), Advanced Information Systems Engineering, Lecture Notes in Computer Science, vol. 2348. Springer-Verlag, Berlin & Heidelberg, Germany, pp. 3-3. Lecture slides available at:
Obrst, L., 2003. Ontologies for semantically interoperable systems. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, New Orleans, LA, USA, 366-369.
Uschold, M., Gruninger, M., 2004. Ontologies and semantics for seamless connectivity. SIGMOD Record 33 (4), 58–64.
Borgo, S., Guarino, N., Vieu, L., 2005. Formal ontology for semanticists. In: Lecture notes of the 17th European Summer School in Logic, Language and Information (ESSLLI 2005), Edinburgh, Scotland, 12pp.

An ontology spectrum (from McGuinness 2003)

To help myself understand the ontology spectrum better, I redrew the diagram (see below) in my PhD dissertation. Very recently (Dec 03, 2013) Jim McGusker, a PhD student with McGuinness, made a thorough explanation of the spectrum in his blog (see:

Ontology spectrum (adapted from Borgo et al., 2005; McGuinness, 2003; Obrst, 2003; Uschold and Gruninger, 2004; Welty, 2002). Texts in italics explain a typical relationship in each ontology type (from Ma 2011)

Finally, I would like to share a few examples for different types of ontologies following the spectrum:

Neuendorf, K.K.E., Mehl, J.J.P., Jackson, J.A., 2005. Glossary of Geology, 5th edition. American Geological Institute: Alexandria, VA, USA, p. 800. See latest version at:

BGS Rock Classification Scheme, see:

AQSIQ, 1988. GB/T 9649-1988 The Terminology Classification Codes of Geology and Mineral Resources. General Administration of Quality Supervision, Inspection and Quarantine of P.R. China (AQSIQ). Standards Press of China, Beijing, China. 1937 pp.

Conceptual Schema:
NADM Steering Committee, 2004. NADM Conceptual Model 1.0—A conceptual model for geologic map information: U.S. Geological Survey Open-File Report 2004-1334, North American Geologic Map Data Model (NADM) Steering Committee, Reston, VA, USA, 58 pp. See:

Ontologies encoded in RDF format:
Semantic Web for Earth and Environmental Terminology (SWEET). See:

Now a short wrap up about what is ontology:
For fun: the invisible hand behind anything;
In philosophy: (uncountable) the science or study of being; that branch of metaphysics concerned with the nature or essence of being or existence; (countable) a theory or conception relating to the nature of being;
In computer science: shared conceptualization of domain knowledge.

To put ontologies (computer science) into practice, keep in mind an ontology spectrum with enriching meanings: catalog/glossary -> taxonomy -> thesaurus -> conceptual schema -> formal constraints.

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