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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 (http://www.nature.com/ngeo/focus/transparency-in-science/index.html), 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 (http://www.datacite.org), Open Archives Initiative-Object Reuse and Exchange (http://www.openarchives.org/ore) and the Data Catalog Vocabulary (http://www.w3.org/TR/vocab-dcat). 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 (http://wssspe.researchcomputing.org.uk). Simply minting Digital Object Identifiers to codes in a repository makes software publication no difference from data publication (See also: http://www.sciforge-project.org/2014/05/19/10-non-trivial-things-github-friends-can-do-for-science/) . 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 (http://www.nature.com/press_releases/article-metrics.html). 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 (http://www.nature.com/news/funders-drawn-to-alternative-metrics-1.16524) 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 (http://wokinfo.com//products_tools/multidisciplinary/dci/) 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 (http://www.nature.com/nclimate/journal/v4/n6/full/nclimate2141.html). 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 (http://www.nsf.gov/pubs/2013/nsf13004/nsf13004.jsp). 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 (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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