“I work in a university library but was trained as an engineer. When I was doing my PhD, my advisor claimed engineering was a liberal art, which I didn’t understand then but I get it now: statistics and computation are all methods. You need to think about people, products and processes, and the workflows that connect them. So I brought that to library world and the research data management world, and it’s definitely an interesting space for people, products, processes and workflows.
I’ve always felt very welcome in this community. When I came I didn’t have the Library and Information Sciences degree or the background training but even in the early stages of my interaction, the community was very open, welcoming and accepting. I try to return that to anyone who is new.
I hope we continue those positive trends in diversity and inclusion. There seems to be more awareness now about that but I think we’ve all been to that panel where you think, ‘Hmm, this isn’t right – everyone there looks the same.’ It’s frustrating when those more formal channels of conferences, things like panels, sometimes aren’t reflective of who’s in the audience. So here, in research data, it’s a healthy community in many ways but we can always look at what can be done better.”
This post was written by Neema Simon SUMARI, a Tanzanian national working at the Sokoine University of Agriculture (SUA), at the time of writing. Currently, She is a Ph.D. researcher specializing in Remote Sensing, Cartography and Geographical Information Engineering at the University of Wuhan in China. She holds an M.Sc. and B.Sc., both in Computer Science, from the Alabama Agriculture and Mechanical University (A&M) in the United States of America. Her participation was kindly supported by ICTP and Nature Publishing, via CODATA.
I first heard about CODATA in July 2016 when I attended an International Workshop in Beijing in 2016. I was very happy and excited to meet new people there, learning new things and seeing new places. It was the first time I had participated in an International workshop/conference, and the first time to experience this in China where I am now doing my Ph.D. Through that workshop, I made lots of new friends and built a strong network of people in and out of my field of study.
The CODATA-RDA Research Data Science Summer School in Trieste, Italy, in July 2017, was the best for me. The summer school was amazing, we exchanged academic knowledge as well as building on our existing networks. I wanted to learn and meet new people, ideas and experience different cultures and CODATA and Springer-Nature supported me in attaining these goals. It has been amongst the best experiences in my life. I met a lot of fascinating people from all over the world, expert professors whose lectures were very interesting and helpful to my academic career. I created strong friendships that I hope I will be able to maintain over the next few years if not more.
At the closing session ceremony, Dr. Simon Hodson, Executive Director of CODATA, asked the participants: “so, what have you learned? and what will you do next?” What I have learned was the idea of Open Science and its principles was a major theme of the summer school. I have learned different issues on why data cannot be shared, how can be analyzed, which data has long term value as well as benefits of storing, protecting, sharing, and publishing data among research scientists. It’s true that most of the researchers would like their data to be publicly stored and accessible by other researchers, however, this is not easy for researchers who do not have clearly defined ways to do this, or do not know how to make their data accessible to others. Knowledge of data management plans for the hosting research institutes is required to ensure that researchers can define ways to store their datasets in a publicly accessible way after their experiments are done. Once the research data is stored in a publicly accessible manner, it then needs to be preserved in a format which can be reused by other researchers. In this summer school, the courses that were taught were: Programming-in-R, Cloud Computing, UNIX Shell, ggplot2, Data Visualisation, SQL, Machine Learning, Data Science Profession, Artificial Neural Networks, Research Computational Infrastructure, HOC and HTC, Research Data Management. These courses gave us very good skills and knowledge about Data Science which can help us to facilitate the sharing of data – it was a great experience. I now know why Open Access and data sharing is important and I will apply and share this knowledge to my professional and social media networks.
Last but not the least, was the wonderful arrangement of having helpers to assist us with any logistical problems occurring during the practical sessions and the use of pink sticker was an outstanding method. It was one of the most enjoyable and informative moments of my life.
Thanks to CODATA, RDA, ICTP, and Springer-Nature for your support, as well as to all my fellow participants for making it possible and fun.
This post was written by Shaily Gandhi, who is currently pursuing a PhD in Geomatics from CEPT University, India. Shaily recently attended the CODATA-RDA School of Research Data Science, hosted at ICTP, near Trieste, Italy – her participation was kindly supported by ICTP and Nature Publishing, via CODATA.
The CODATA-RDA School of Research Data Science was a great opportunity for me to work with around 45 students from 29 countries (mostly from lower and middle income countries) and from varied educational backgrounds. Such summer schools or short courses can be the best platforms for learning innovative ways of teaching as well and understanding the work done by different people in the same area. The summer school introduced me to various aspects of data science and intensive hands on training: it has stimulated in me the confidence to start working with concepts which I had just read in books. Now I will be able to implement machine learning and artificial neural networks in my PhD study in Geomatics for developing predictive models.
The school uses the Software Carpentry / Data Carpentry approach of having the students provide daily feedback on pink or green stickers (which signify XXXX). This was a factor which made each us feel that our opinions count. I am very thankful to the organizers who have been on their toes and have been working long hours to make the summer school run smoothly. While working closely with leading academics in the field of data science, it was one of the most wonderful experience for me which not only taught me but also it helped in improving my teaching skills. I have observed many small things in their teaching which I would like to implement in the coming semester’s teaching.
Practical teaching and the use of sticky (this picture was taken during the git session summer school 2017 Trieste)
One of the things which caught my eyes on very first day was the way of using the pink and green sticks for indicating if you are good with the practical or if you need help. I will definitely use this in my teaching because teaching practicals becomes very difficult to handle with a large class and if everyone is waving or calling it makes the environment very noisy.
Women in Research Data Science
Apart from technical learning there was a wonderful experience of cultural exchange. One of the most interesting topics which I discussed with Gail Clement from the California Institute of Technology (who introduced us to Author Carpentry) was the loss of academic identity that can be experienced by women who change their name after getting married (and in some countries this change of name is obligatory). She explained that according to the research men’s research works are more cited then women’s: there are many reasons for this and the loss of identify can contribute as computer search mechanisms and bibliographic tools do not necessarily link the works of women prior to and subsequent to a name change. This is one of the important reasons for a recognised and standardsised researcher ID system: for women who have changed their names, having an ORCiD account will help will keep all your academic work associated with on single researcher ID number. Gail also suggested that it would be better if female researchers could retain both the last names which could “help you built your identity and reputation in the professional world”. Many more interesting discussions regarding the ignorance of credit for work were also brought up. In few institutions are the people doing data analysis included as co-authors to the publication: Gail suggested that a standard criteria should be developed and implemented, such that all contributors (including data analysts and data stewards) are credited and the credit for your contribution stays with you.
Working with Irma and Oscar on some super cool projects (from left to right)
I had a great learning experience by working with people from different countries in groups. Throughout the school, we were working in different groups with different people which gave us lot of exposure to understand the varied situation of data science in different countries. We worked on a project which allowed us to make work on the same file using Git and in the second project we coded the neural network model in python.
The Bring Your Own Data session offered good suggestions regarding my problems with data and the confusions which had been addressed by other students in the summer school working in the same area. I learned a lot about statistical analysis from other students, including Felix Anyiam (Data Analyst, University of Port Harcourt Teaching Hospital (UPTH)) and Ola Karra (Lecturer, Department of Statistics, University of Khartoum).
Friends at help with Statistics Laba, Felix and Ola (from left to right)
This summer school gave us first-hand experience on many languages and command line interfaces: topics included DOS, R, Shell, Github, visualisation of data in most beautiful ways, machine learning, artificial neural networks other machine learning systems and recommender systems.
Working with Github was an excellent experience. I had been using google drives to work on shared presentations but Git looks pretty cool and would like to use it for my future work to share data and work in a shared environment.
It was great working on the research computation infrastructure with all the participants working on different systems and learning how to submit the job and get the job done using external resources. We were taught how to get access to super computers from different geographical locations: this enables researchers to keep going as it allows you to work from any part of the world. Resources to run the processes can be allotted from different locations.
Finally, we also got a good insight into research data management, referencing systems and wonderful tips for publishing and licensing work.
Friends of Data Science
Map of Student participants:
I am very thankful to ICTP for accepting my application and supporting my stay in Trieste. I am very grateful to Nature Publication, via CODATA for funding my travel which gave me an opportunity to attend this summer school on big data Science.
“Brené Brown, the social scientist, said that stories are data with a soul. I think about that a lot in the work I do. I’m passionate about it. When I meet the most engaging researchers, they’re good storytellers. Data are ways to connect with stories – data are the underlying content that researchers are sharing through their stories. I’m keen on preserving those stories, sharing those stories, now and in the future.
Particularly now, we’re in an unfortunate situation in the United States where things we had taken for granted – trust and integrity of information – are being questioned. And we’re seeing such an emerging problem with tribalism, where people in their bubbles only talk to each other.
Data are a way we can span between different communities, different tribes, different people. We do that already in the research space, I think, but I hope that by continuing our work in data, we can help to deal with this tribalism issue.”
Thifhelimbilu Mulabisana is a Junior Scientist in the Geophysics Division of the Council for Geoscience in South Africa. Her day-to-day work involves the recording, processing and analysis of seismological data. The organization manages a network of over 50 seismic stations around the country and these are continuously streaming data into her office for processing. Thifhelimbilu attended the CODATA International Training Workshop in Big Data for Science in July 2016. And in July 2017 she was able to follow this by attending the School for Young Scientists “Methods of Comprehensive Assessment of Seismic Hazard”, organised by the CODATA member organisation for Russia, the Geophysical Centre of the Russian Academy of Sciences. This is the second of two blog posts in which you can read about the experiences of one young researcher from South Africa in training activities that took her from Beijing to Moscow and back.
Thifhelimbilu (right) with friends in Moscow (Xia from China, currently doing a PhD at the State University of Moscow (left), Nguyen from Institute of Geophysics, Vietnam (middle)
As a young scientist, most of my time is spent on the internet looking for articles to read so I can better understand seismology. This is how I came across a poster about the School for Young Scientists “Methods of Comprehensive Assessment of Seismic Hazard” http://school2017.gcras.ru/, I was immediately drawn to researching further on what the school is about. I knew that I would like to attend and improve my knowledge of seismology, when I found out that the school will be devoted to the new methods recently developed for seismic hazard assessment and integration on the basis of the systems analysis of results obtained by these methods.
When I applied for the training I was worried about the cold weather in Russia. I recall when I got the email stating that I had been accepted for the training course, I went onto Google and searched how the weather is like in Russia. Being a South African who is currently based in Pretoria where winter means temperatures are low in early morning hours and at night only, unless there is a cold front, you can understand my despair with cold weather!
Thifhelimbilu (rights) with Natalia from Vladivostok University
Of course, the weather was not the only thing I was worried about, language was also in that basket. Therefore, a couple of weeks before the training I tried to learn a few Russian words that could get me by. This evidently became a futile exercise when I landed at the airport and couldn’t read a word on the signs. I had to ask around to figure out my way to the train. I really appreciated the woman I met when we were in the queue for passport control. She had been in Russia before; therefore she knew her way and showed me where I can get the train. From there I was asking anyone I met and Russians were the friendliest to me.
The programme of the school covered exactly the reason why I became a seismologist in the first place. When I first heard about seismology, I was eager to at least figure out how we can predict earthquakes as they are by far the most dangerous natural hazard. Therefore, looking at the programme I knew that I had to go there, I had to meet people who are studying every day of their lives exactly what I had always wanted to do (see the programme and presentations at http://school2017.gcras.ru/e.materials.html).
The school exceeded my expectations; the lectures were just the kind of smart I have been yearning for the whole duration of my career as a seismologist. The work that they are doing is beyond what anyone can imagine. Can you imagine the day we can predict an earthquake? I bet you disaster management in every country will be ecstatic to that discovery.
The topics that mostly caught my eye were the identification of earthquake prone areas, calculating maximum magnitude using statistics and how the accuracy of this gets tainted due to lack of data; and the investigations on how to predict earthquakes, with the highest confidence level possible. The biggest obstacle in conducting breakthrough research in seismology is lack of data, of which the biggest known cause for this is the station coverage in most of the countries and also sharing data. The problem of data sharing is one thing which CODATA is working towards improving.
Overall, this was a brilliant trip and I would like to extend my gratitude towards the organisers of the school; the Russian Science Foundation within the framework of RSF Project “Application of systems analysis for estimation of seismic hazard in the regions of Russia” and the Council for Geoscience for funding the school and my travel to Russia, respectively.
Group photograph from the School for Young Scientists “Methods of Comprehensive Assessment of Seismic Hazard
Thifhelimbilu introduces herself at the Beijing Training Workshop
Thifhelimbilu Mulabisana is a Junior Scientist in the Geophysics Division of the Council for Geoscience in South Africa. Her day-to-day work involves the recording, processing and analysis of seismological data. The organization manages a network of over 50 seismic stations around the country and these are continuously streaming data into her office for processing. Thifhelimbilu attended the CODATA International Training Workshop in Big Data for Science in July 2016. And in July 2017 she was able to follow this by attending the School for Young Scientists “Methods of Comprehensive Assessment of Seismic Hazard”, organised by the CODATA member organisation for Russia, the Geophysical Centre of the Russian Academy of Sciences. This is the first of two blog posts in which you can read about the experiences of one young researcher from South Africa in training activities that took her from Beijing to Moscow and back.
Two years before I went to the Beijing training workshop, one of my colleagues went to the same workshop and his feedback about the training was nothing but great. I then became eager to attend the training and so applied as soon as they advertised the course.
The training workshop was focused on promoting improved scientific and technical data management and use. This was exactly what I needed at the time as I was studying towards my MSc. My dissertation was focused on the earthquake catalogue of southern Africa and this was the biggest data I had ever worked with. It became more tedious and frustrating with time and I knew I needed to find better ways to deal with that amount of data.
Thifhelimbilu (right) and Nobubele (from CSIR in South Africa) at one of the social dinners
From the day I found out that I was going to China I was excited, I had never been to Asia. The thrill of going to a country where their medium of instruction is not English was both a challenge and nerve racking (though the Training Workshop is taught in English).
When I arrived in Beijing, my expectation about it was exceeded. Except of course for the stares I got for being black and having long dreadlocks! I suppose people in this part of the world do not get to see a lot of dreadlocks, as some of them even went as far as trying to take pictures of me. There were those who tried to be a bit polite and ask but some of them just went ahead and took the pictures. The whole experience had a certain level of violation but mostly taught me about the diversity we have as a human species.
Thifhelimbilu receives the participation certificate from Prof. LI Jianhui, Secretary General of CODATA China and a member of the CODATA Executive Committee
As most of the Chinese people do not speak nor understand English, and as much as I tried to learn the Chinese language using Google translate, the language barrier was a huge obstacle every time I had to get food. This issue was so evident so much that, most of the time I did not know what exactly I was eating! The first few days, this did not sit well with me but as time went by I was only concerned about how food tasted.
Day one of the training was blissful; I met brilliant young scientists from different fields. This encouraged me to do more for science and be better. Not forgetting meeting the lecturers, the giant scientist I have been longing to meet since I read the first pamphlet about the training course.
The real work began and as I had expected topics such as interdisciplinary applications of open research data, data intensive research, data management policies, cloud computing, visualization, analytics and data infrastructure development in the Big Data Age were covered precisely and greatly so. The practical sessions we had had the most impact by ensuring that I understood the topics well enough and I left every lesson confident that I will be able to do the same when I get back to my home country.
I can confidently confess that this course helped me with my MSc studies, which I completed successfully. I am most grateful to the organisers, CODATA, the Chinese Academy of Sciences and the Council for Geoscience for their sponsorship.
The 2016 Beijing Training Workshop Students at the Great Wall of China
“I work in a university library but was trained as an engineer. When I was doing my PhD, my advisor claimed engineering was a liberal art, which I didn’t understand then but I get it now: statistics and computation are all methods. You need to think about people, products and processes, and the workflows that connect them. So I brought that to library world and the research data management world, and it’s definitely an interesting space for people, products, processes and workflows.
