Sagol School of Neuroscience · Tel Aviv University

Open science, built into the work.

Open Science Practices remove barriers in science: sharing methods, data, and code openly so research is reproducible, reusable, and free to build on.

04Guiding principles
11+Sharing guides
2025Joined TOSI Alliance
Our philosophy

Principles for open, reproducible neuroscience

Open Science (OS) encompasses a broad range of practices intended to remove barriers in science and allow the dissemination and use of research outputs. The Sagol School of Neuroscience (SSN) believes that following Open Science principles greatly contributes to better planning and execution of experiments; invigorates collaboration and innovation within and across disciplines; and is fundamental to the ethical usage of public funds in research.

Embracing Open Science not only enhances the quality and impact of research but also fosters a culture of transparency and collaboration. By participating in Open Science, researchers contribute to a global movement that accelerates scientific discovery and benefits society as a whole.

To ensure our scientific effort is excellent, reproducible, and FAIR (findable, accessible, interoperable, and reusable), the SSN is institutionally oriented towards Open Science and is committed to providing the required support for individual researchers in adopting current open science practices and future best practices. SSN will offer ongoing guidance and help align institutional practices with all relevant principles and commitments.

SSN will ensure that researchers adhere to responsible research practices, including responsible data management, compliance with intellectual property laws, and respect for participants' rights and dignity. Open Science must not compromise legal or ethical obligations or any applicable data protection principles.

01

Release of research processes and outputs

Subject to the applicable laws, and in particular privacy and data protection regulation, SSN strongly encourages researchers to publish open access articles and share their scientific resources – research methods and materials, software, analysis tools, physical resources, as well as raw data and metadata – as early as possible, and no later than the date of publication of the first article that relies on this data or resource.

Open practices should be considered in advance, during the planning and design stages of a study. Best practices include data management and sharing plans in reusable, machine-readable formats and in accordance with the FAIR principles, considering using and contributing to open-source software tools and using existing open data sources.

Researchers are highly encouraged to make scientific findings available early and openly through preprints of their work and use self-archiving or open access publishing to make their articles open access.

Scientific resources generated through any partnership by SSN researchers should aim to adhere to SSN Open Science Principles.

02

Inclusivity, collaboration and dissemination in science

SSN supports sharing of research outputs beyond SSN researchers, to foster a culture of inclusivity and shared knowledge with external collaborators and the public. SSN supports non-exclusive, irrevocably available, worldwide access and freedom-to-operate research outputs for further research, education, and humanitarian efforts. SSN believes research should be communicated in a way that allows the wider public to understand, own, and contribute to scientific progress.

03

Open Science Compatible Intellectual Property

SSN's position is that restrictions on reuse should be minimized whenever possible to allow for fast, impactful, and beneficial work. Researchers should positively consider alternatives to restrictive IP that maximize the unlimited freedom-to-operate of any entity, public or private, to use outputs or other results of research conducted by the SSN community and collaborators. SSN will support any efforts to minimize restrictions on the reuse and sharing of intellectual property.

04

Researchers and Participants Autonomy

Researchers retain full autonomy and academic freedom in how they approach Open Science. SSN recognizes and respects the autonomy of its stakeholders – including researchers, staff, trainees, and research participants – and upholds their right to opt out of participation in activities conducted under SSN's Open Science Principles or to refrain from applying SSN's Open Science principles for any purpose. However, SSN will ensure that funds designated for Open Science are used exclusively to support initiatives that align with these principles.

Guides for data & code sharing

Best-practice guides, ready to use

SOP-style guides fit the common types of data collected across our labs, and are updated continuously. Start with the Best Practices guide, then jump to the format that matches your data.

01 — Data Sharing Guides · by data type

Data typePlatform / formatLast updateGuide
Genetic (Human)dbGaP30/05/25PDF
Genetic (Non-Human) SequencingSRA16/07/25PDF
Histological DataZenodo · Figshare · Morphosource07/10/25PDF
GeneralOSF06/05/25PDF
Behavioral DataPsych-DS & OSF20/05/26PDF
Behavioral DataBIDS & OSF03/09/25PDF
fMRIOpenNeuro & BIDS29/06/25PDF
Eye TrackingBIDS & OSF29/05/25PDF
Neurophysiology — LFP/EEG, Calcium Imaging, Patch Clamp, Single UnitsNWB and DANDI18/06/25PDF
Fluorescence Spectroscopy DataFAIR-principles compliant01/05/26PDF

For VR data, see the TAUXR data-export guide & toolkit.

02 — Preregistration Guides

GuidePlatform / formatLast updateGuide
PreregistrationOSF & AsPredicted16/07/25PDF

03 — Code Guides

TypePlatform / formatLast updateGuide
Clean Code (R)R03/09/25PDF
General Code SharingGit03/09/25PDF
FAIR Code (Python)Jupyter Notebook03/09/25PDF
Open science courses at TAU

Learn it, then teach it

Courses that build critical thinking and hands-on open-science skills, from sharing your first dataset to interrogating how science is funded, measured, and reformed.

1501-1051

Theory & Practice: Opening Neuroscience

Part of the Tanenbaum Open Science Institute (TOSI) project, this course helps a small group of M.Sc. and PhD students openly share a dataset and learn core Open Science practices. Each student (or pair of students) runs a real data-sharing project: formatting and uploading data to a suitable repository, ideally using a dataset from a willing lab so the work has genuine scientific impact. Most sessions focus on the practical, technical side of sharing data, following guides provided in the course.

1501-1022

Science: the Good, the Bad & the Ugly

What is science for, who funds it, and does it actually live up to its mission? This course looks critically at how science works in practice: the gap between what gets rewarded and what actually serves good research, the push for replication, data and code sharing, and transparency, plus how social media and open publishing are reshaping how findings reach the public. The goal is to build critical thinking through class discussion, not just cover the theory.

0455-2778

Research Methods: Theory

How does research get designed, and where does scientific knowledge actually come from? This course examines what threatens the validity of a study, including the replication crisis, poor reproducibility, and variability in how data gets analyzed, using real cases from the Retraction Watch database. Students are expected to take an active role in class discussion.

Annual poster

Promoting Open Science at Sagol

A progress report of the first year of the OS project in SSN.

View the 2026 poster