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e-Science Lab
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An e-Science lab that approaches science with computers at its center.
AboutAbout the lab
So that anyone can take part in scientific discovery.
The e-Science Lab studies "e-Science," an approach to science that puts computers at its center.
e-Science is an approach to science centered on computing, such as simulation and data analysis, that does not rely heavily on specialized knowledge.
The lab aims to make it possible for ordinary people to take part in scientific discovery and verification.
In addition to data mining and simulation, the lab also studies the databases and search mechanisms that support them.
Key terms
- e-Science
- Science that does not rely heavily on specialized knowledge and instead centers on computer simulation and data analysis
- Data mining
- A technique for automatically extracting patterns and useful knowledge from large amounts of data
- Stigma
- Negative labels or prejudice directed at particular people or groups
- Text mining
- A technique for analyzing text data with computers to extract frequent topics and trends
NewsLab news
A paper by a doctoral student was accepted at the international conference DSTM 2026
A paper by a graduate of the lab was accepted at the international conference DSTM 2026
A paper summarizing how to handle bots in tweet analysis, from joint research with Mie University, was accepted at the international conference ESKM 2026
A paper by a doctoral student was accepted at the international conference IAR 2026
A paper by a doctoral student was accepted at the International Journal of Service and Knowledge Management
ResearchResearch themes
Guided by the vision that ordinary people should be able to take part in discovering and verifying scientific findings, the lab works on e-Science. It carries out simulation and data mining, and also studies the infrastructure that supports them.
*Illustrative imagePredicting rare events
The lab works on predicting events that rarely occur, such as solar flares, earthquakes, and popularity. Examples include studying natural phenomena from scientific data, and studying the spread of trends and misinformation from social media data.
*Illustrative imageShifts in sentiments and claims
The lab studies how negative feelings such as prejudice and stigma, and political claims, change over time. Examples include text mining of Japanese tweets and of presidential speeches.
*Illustrative imageRecognizing abstract illustrations
This research deals with recognizing illustrations that are more abstract than realistic images. The approach starts from realistic images and gradually raises the level of abstraction.
*Illustrative imageSupporting foreign-language speaking practice
This research supports second-foreign-language speaking, focusing on the speaker's confidence. The lab has Japanese practice data, and recognizing the speech of non-native speakers is also a candidate theme.
MembersFaculty
The e-Science Lab consists of faculty members and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorDaisuke Ikeda
VoicesStudent voices
Compiled from answers by students who were in the lab. Ratings and reviews reflect individual students' experiences, not the lab's official views.
Students who take part in an interview may receive a thank-you gift worth ¥1,000, regardless of what they say.
On a 4-point scale, ● marks where students' answers fall
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Rear of rack at NERSC data center - closeup” Derrick Coetzee from Berkeley, CA, USA / CC0 (cropped)
- Predicting rare events: “Aurora in Abisko near Torneträsk” Pavel.shyshkouski / CC BY-SA 4.0 (cropped)
- Shifts in sentiments and claims: “Parrot-shaped word cloud of the Global Assessment Report on Biodiversity and Ecosystem Services” Daniel Mietchen / CC0 (cropped)
- Recognizing abstract illustrations: “Pech Merle cave, painting” HTO / Public domain (cropped)
- Supporting foreign-language speaking practice: “Short-window reassigned spectrogram of speech” Kelly Fitz / CC BY-SA 3.0 (cropped)
- Related lab (コンピュータビジョン・グラフィクス・VR 研究室): “3D Scanning Team” IT Photography / CC BY-SA 4.0 (cropped)
- Related lab (ソフトウェア工学・
プログラミング言語研究室): “Keyboard in dark” The indian dev / CC BY-SA 4.0 (cropped) - Related lab (ヒューマン・
データ・インタラクション研究室): “APRENDIZAJE SIGNIFICATIVO” SAMMYTQM / CC BY-SA 4.0 (cropped) - Related lab (高性能計算研究室): “IBM Blue Gene P supercomputer” Argonne National Laboratory's Flickr page / CC BY-SA 2.0 (cropped)
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