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 (http://datacentrum.3tu.nl/en/home/), 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) (http://www.cdlib.org/), 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 (http://www.cdlib.org/uc3/) 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 (https://www.force11.org/datacitation) 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 (http://commons.esipfed.org/node/308), which offers more practical details on how a piece of published dataset should be cited. In 2013, the Research Data Alliance (https://rd-alliance.org/) was launched to build the social and technical bridges that enable open sharing of data, which enhances existing efforts, such as CODATA (http://www.codata.org/), to promote data management and sharing.

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To promote data citation, a number of publishers have launched so called data journals in recent years, such as Scientific Data (http://www.nature.com/sdata/) of Nature Publishing Group, Geoscience Data Journal (http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%292049-6060) of Wiley, and Data in Brief (http://www.journals.elsevier.com/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 (http://wokinfo.com/products_tools/multidisciplinary/dci/) 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 (http://www.nsf.gov/pubs/2013/nsf13004/nsf13004.jsp). 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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A layer cake of spatial data, and in a jigsaw puzzle style

September 4th, 2014

During a lunch at the GeoData 2014 workshop, Boulder, CO, USA, June 2014, people sitting around the table began to chat about topics relevant to data sharing, data format, interoperability – all those topics relevant to geoscience data – well, inter-agency data interoperability was the central topic of that workshop. When someone rose up the topic of comparing data sharing policies in USA with those in Europe and China, a few people (those who know me) looked at me and began to smile. Yes, I am confident to say that I have some comments on the geoscience data sharing in Europe.

Before I came to USA I spent about four and half years in the Netherlands working for a PhD degree on geoscience data interoperability . When I looked back, it seems very interesting because I knew nothing about what was happening on data sharing in Europe before I headed to ITC. But the world is a really small cycle. At the second year of my PhD study, I got in contact with a colleague in the Commission for Management and Application of Geoscience Information of the International Union of Geological Sciences, and he worked at the Geological Survey of the Netherlands at Utrecht. I visited him several times and from him I also came to know about the giant data sharing initiative of EU, the Infrastructure for Spatial Information in Europe (INSPIRE).

Initially, what attracted me is some technical details in INSPIRE, especially those surrounding the works on vocabulary modeling and web map services. INSPIRE covers 34 data themes, among which geology is my favorite because geological data is the topic of my PhD work at ITC. And I really appreciated the data specification working group of the Geology theme in INSPIRE, as colleagues in that group offered me so many fresh technical ideas. Then, in my fourth ITC year, when I began to prepare my PhD dissertation and the defense, a guideline ‘Don’t get lost in details, look at the big picture’ inspired me review the INSPIRE from another angle and discuss my ideas with advisors and colleagues at ITC.

I forgot to mention that many such discussions happened during coffee breaks or lunch breaks at ITC (Well, there is no such a culture in the USA). And then, one day, during such a coffee break chat, a view came into my brain – a jigsaw puzzle layer cake – a nice analog of the INSPIRE initiative: the 34 data themes represent 34 layers and the 27 EU nations (in 2011) represent 27 puzzle pieces. The data specifications and implementation rules of INSPIRE are the recopies for making cakes, and the public agencies in EU nations are the cake cooks.

A 'jigsaw puzzle layer cake view' of the EU INSPIRE initiative

This ‘cake’ view sounds like a jest, but I took it seriously and I know in GIScience people used to call data as layer cakes. I drafted a manuscript to describe my view immediately after that coffee break chat, but it was out of my plan that the short article was not published until four years later – actually, just one month before the lunch table meeting at GeoData 2014, and
EU has 28 nations now (Croatia joined in 2013). The article is accessible at http://onlinelibrary.wiley.com/doi/10.1002/2014EO190006/abstract.

The INSPIRE initiative is combination of bottom-up and top-down approaches. The bottom-up approach is reflected in the works of data specification drafting and technical infrastructure constructions, which represent the consensus of experts from the EU nations. The top-down approach is reflected in the formally issued EU directive for the INSPRE, which makes it a de jure initiative, that is, EU member nations are required to comply with the INSPIRE data specifications and implementation rules when build their national spatial data infrastructures.

USA has a different administrative system comparing with EU. That, more or less, is also reflected in the geoscience data sharing policies and technologies. However, people here also build such data cakes. What can USA benefit from the EU experience and what suggestions can it provide based on its own work? I do not have a single answer now but I hope I will have some comments a few years later. Fortunately, similar to my encounter with the colleague at the Geological Survey of the Netherlands, now I also come to know colleagues at NASA, USGS, NOAA, EPA, USGCRP, and more, who are showing me the picture of geoscience data issues in the USA.

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Geoscience in the Web era – a few facets

July 30th, 2014

In middle July 2014 I attended the DCO summer school at Big Sky Resort, MT, with a 2-day field trip at Yellowstone National Park (YNP) – a nice experience – the venue is wonderful, and also the topics covered by the curriculum. But what impressed me the most is to see how the Web brings changes to geoscience works as well as geoscientists.

We have three excellent field trip guides, Lisa Morgan, Pat Shanks and Bill Inskeep. They prepared and distributed a 82-page YNP field trip guide! Of course they first shared it online through Dropbox. What also impressed me is that when I showed my golden spike information portal to Lisa, she also showed me a few APPs on her iPhone with state geologic map services – useful gadget for field work. But our field trip experience in YNP showed that a paper map is still necessary as it is bigger and provides a overview of a wider area, and it needs no battery.

The YNP itself has a virtual observatory website called Yellowstone Volcano Observatory, hosted by USGS and University of Utah. The portal provides “timely monitoring and hazard assessment of volcanic, hydrothermal, and earthquake activity in the Yellowstone Plateau region.” Featured information includes publications, online mapping services, and also images, videos and webcams about YNP.

I was happy to see that Katie Pratt and I are accompanied by many other summer school participants when we were tweeting on Twitter. Search the hashtag #DCOSS14 you will find how active the participants were on Twitter during the period of the summer school. I was even a little surprise to see that Donato Giovannelli ‏@d_giovannelli helped answer a question about twitter impact on citation by pasting the link to a paper, a few seconds after I gave a short introduction to the Altmetric.com and its use in Nature Publishing Group, Springer and Wiley.

And my role at the summer school was two-fold: participant and lecturer. I gave a presentation titled ‘Why data science matters and what we can do with it‘, in which I addressed four sub-topics: data management and publication, interoperability of data, provenance of research, and era of Science 2.0. The slides are accessible on Slidershare [link].

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Notes on public talks

July 16th, 2014

Massimo and I worked together on two posters about automatic provenance capturing for research publications and we won the ESIP FUNding Friday award. What left unforgettable to me, however, is the great lesson I learnt from giving the 2 minute pitch in front of the ESIP folks.

During the 2 minutes talk, I just could not help staring at the two posters we printed and made on the day before and that morning. Now I know the reason — it’s because I only practiced my speech with one of the posters displayed on my laptop. For the other poster, I have no chance to practice talking about it at all. I became dependent on the presence of the posters in front of me and cannot make the talk in front of people, instead of posters.

Possible solutions to make my eyes move away from the posters when talking? The best I thought of is to get REALLY familiar with the topic I’m gonna present — at least so familiar that I don’t need to look at any auxiliary facility such as a poster to remind myself what to say, better if being able to save some spare attention for the audience — to receive their feedback and adjust accordingly in real time. The need to ignore the audience for a while to concentrate on “what should I say here?” indicates that I’m not familiar enough with the topic.

In addition to the content, presenters also need to get familiar with the way of presenting the content. This could include scrutinizing the practice talk sentence by sentence to make sure “I said what I meant and I meant what I said”. Not until such clarity and confidence are reached can one start thinking about all the fancy stuff like speaking pace, volume variations and eye contacts with audience. Well, those are fancy to me, not necessarily for good speakers.

So there is really a lot to work on for a public talk, especially if it’s the first time for the presenter to talk about the idea. The work is so much that it cannot be done over the night before the talk. We need to work on the familiarity, clarity and confidence of our ideas on a daily basis. It helps to write down what we mean and talk about it often.

 

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Geodata 2014

June 30th, 2014

A few weeks ago I attended the 2014 Geodata Workshop. Like the previous Geodata workshop in 2011, this workshop was focused on discussing policies and techniques to improve inter-agency geographic data integration and data citation. While there have been advances in recommendations for data citation and geodata integration since the last Geodata workshop, I felt the mood of the attendees indicated that we are now in much the same place we were in 2011. There was strong consensus as to the importance of data citation and integration, but a feeling that no one is really doing it at scale, the tools aren’t where we need them to be, and the agency policies are not yet at a state to successfully drive widespread adoption. Despite these hurdles this is a community that is clearly excited and willing to take the first steps towards making widespread data integration and data citation a reality in the geodata community.

Meanwhile, in the trenches…

I had several conversations with attendees who represent publishers of oceanographic vocabularies. Many of these vocabularies have been publicly available for several years, but have been traditionally been 3-star open data (publicly available in a non-proprietary machine-readable format, no links to external vocabularies). These publishers are excited about upgrading their vocabulary services to be 5-star open data (use open W3C standards such as RDF/SPARQL, identify things with resolvable URIs, link to other people’s data) because they see a major benefit in being able refer to the authoritative source for a term or identified resource that is related to their vocabulary but for which they are not the authoritative source. This is a great example of a group that has already identified a specific real-world need and benefit from integration and who are actively laying the groundwork that will enable that integration to be successful. This group was enthusiastic about cross-linking their vocabluaries and I have no doubt their efforts will be viewed as a data integration success at the next Geodata workshop.

Where we can help…

As a result of these discussions our lab is starting a Linked Vocabulary API effort whose goal is to provide a Linked Data API configuration specialized to the purpose of publishing SKOS vocabularies. Our goal is to develop a configuration that makes bootstraping a RESTful linked data API to a SKOS vocabulary simple and accessible for the broad scientific community.  This effort is based on work we previously did for the CMSPV project.

In conclusion

What I will remember most from Geodata 2014 is the excitment members of the community had towards adopting new technologies and techniques and making widespread data integration and citation a reality. Where conventions have yet to be established the community is willing to take the first steps and establish best practices.  Where policies have yet to be formalized the community is ready to work with policy makers to ensure clear and helpful policies are established .  Whenever the next Geodata workshop is held, I am confident that it’s narrative will be full of success stories that began at the 2014 workshop.

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