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CREATING THE COMMON LANGUAGE OF SCIENTIFIC RESEARCH.


DATAJOINT ENABLES RESEARCHERS TO COLLABORATIVELY RECORD, ANALYZE AND SHARE
VALUABLE RESEARCH DATA.

Key BenefitsGet a Demo

THE DATAJOINT PLATFORM


MEET A NEW OS FOR SCIENCE.


DATAJOINT MAKES SCIENTIFIC RESEARCH REPRODUCIBLE AND SCALABLE.


INFORMATION CAPTURE


A NEW HOME FOR ALL OF YOUR EXPERIMENT DATA

Record everything in one place. From your lab metadata and specific equipment
configurations to trial and experimental subject records, DataJoint is your
all-in-one repository.

Schedule a demo
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DATA PROCESSING


AGGREGATION, SYNCHRONIZATION & HARMONIZATION

Process and synchronize parallel data sources, extract relevant information, and
evaluate each side-by-side on the same time clock, regardless of instrument or
modality.

Book a demo

COLLABORATION


COLLABORATE, ANALYZE & PUBLISH

Merge team member workflows without conflict, analyze data streams individually
or as a whole, and set the stage for publishing and reproducing results.

Learn more
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PARTURIENT TORTOR

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nisi erat porttitor ligula, eget lacinia odio sem nec elit. Cras mattis
consectetur purus sit amet fermentum. Donec id elit non mi porta gravida at eget
metus. Etiam porta sem malesuada magna mollis euismod.


TESTIMONIALS


TRUSTED BY THE BEST


“WE ARE BIOLOGISTS, NOT IT PROFESSIONALS. A LOT OF SCIENTISTS LIKE US ARE
STRUGGLING WITH THIS KIND OF DATA.”

Dr. Hui-Chen Lu, Professor



Gill Center for Neuroscience


"IT’S NOT EASY TO JUMP ON SOMEONE ELSE’S EXPERIMENT. WHEN A STUDENT LEAVES THE
LAB, IT TAKES MANY MEETINGS AND CALLS TO LOCATE AND MAKE SENSE OF THE CODE AND
THE DATA THEY LEFT BEHIND. WITH DATAJOINT, WE ARE BUILDING A PROCESS WHERE THE
DATA AND CODE ARE MANAGED JOINTLY AND ARE READILY UNDERSTOOD BY EVERYONE ELSE IN
THE LAB."

Dr. Lauri Nurminen, Assistant Professsor



University of Houston


"THERE'S MORE WILLINGNESS TO EXPERIENCE PAIN UP FRONT TODAY FOR BETTER SHARING -
AND THAT WILLINGNESS WAS NOT THERE 4 YEARS AGO. WE NEED TO BE CLEAR TO THE NIH
THAT WE'RE SERIOUS ABOUT THAT."

Dr. Sandeep Robert Datta, Professor



Harvard Medical School

Slide 2 of 3.



Over 100 neuroscience labs rely on DataJoint

Frequently asked questions


DIVE DEEPER INTO DATAJOINT

What kinds of experiments can DataJoint be used for?


DataJoint Works is a general-purpose data operations platform. However its roots
lie in supporting neuroscience studies, with extensive use in experiments that
combine instruments for electrophysiology, stimuli, multiphoton microscopy of
neuronal signals, optogenetics, behavior, histology, and many others. A
collection of reference implementations – DataJoint Elements – supports a
variety of data-rich studies.

Can I use DataJoint if I already have an existing data pipeline?


Having an existing data pipeline is even better. There will be some development
effort required to reorganize the existing pipeline into DataJoint tables to
define proper data model and computational dependencies, but all existing
processing/analysis code is fully reusable.

How much coding is required to use DataJoint?


DataJoint requires python proficiency at the same level as other scientific
packages in python (such as numpy, pandas, matplotlib, etc.).

Some basic knowledge of database design and operation is preferred (e.g. primary
key, foreign key, joins, normalization, etc.).

How do I begin?


Materials to learn the basics of DataJoint can be found at DataJoint Python and
DataJoint Tutorials

‍Reference implementations of data pipelines for various neurophysiology data
modalities and analyses can be found at DataJoint Elements

Where will my data reside?


The large data files (e.g. raw files) and bulky processed results are stored as
files on cloud object storage or on-premise file servers, with points to these
files managed seamlessly within the DataJoint framework.

The metadata and/or computational results (smaller in size) are stored directly
within the database (MySQL). The database server itself can be hosted on the
cloud or on-premise.

How can I take all my data out?


Access and interaction with the data can be done via programming APIs in python
(DataJoint-python) or MATLAB (DataJoint-MATLAB).

Furthermore, as DataJoint is built on top of relational databases (MySQL), users
can use any SQL-supported tools to access the data.

Ready to accelerate your research?
Fill out the form and our team will be in contact.
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