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Update 1. Introduction.md

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+# Chapter 1 Introduction to Open Science in Neuroscience
+**Authors: Juliette Castelot, Sara Said**
+**Reviewers: Eva van Heese**
+
+## About the guidebook
+Open science (OS) is a community-minded practice which centres around open communication and data transparency. OS is important for collaboration and innovation in research to accelerate scientific progress and achieve a higher societal impact. In young, developing and growing scientific fields adoption of OS is vital. The aim of this guidebook is to provide comprehensive guidance on integrating OS principles, and how to structure elements being shared with the fields of research. These guidelines will particularly focus on the dynamic landscape of emerging disciplines, such as neuroscience. 
+
+In this guidebook different topics will be discussed: Preregistration and protocols, Data Organisation, Open Software and Tools, Educational Resources, Publishing & Preprint, Communication, Creating an Open Science group, Inclusivity and Recognition & Reward.
+
+## What is open science?
+The term “Open Science” has been around since 2013. It is the exercise of clearly communicating research ideas, project designs, analysis plans and procedures, and of pledging to publish the results regardless of their significance. OS, with proper guidance, can be conducted with simple tools and easily implanted in your research routine (See Chapter 4 for more information on tools). OS revolves around sharing details on datasets, data analysis, coding scripts that contributed to data handling, laboratory protocols (staining, techniques, antibodies, etc) as well as other components which are integral to your research.
+
+Scientists can implement OS in any way they wish and fits well with their type of research. They can publish a preregistration of their project to share analysis plans. They can commit to publishing their findings regardless of their significance. They can also apply OS by publishing their scripts, protocols and dataset so that their research is more transparent and easily reproducible by others. OS can be put to practice in various ways which all have the main goal to facilitate access, create transparency in research, and openly communicate about projects in several stages of project completion. Additionally, other researchers can provide feedback during the conceptual and analysis stages, far ahead of publication. 
+
+## Why open science? 
+OS is a low cost practice that increases transparency and that improves the environment of research. It fosters innovation as it allows labs to better build on each other’s work and to improve the quality of research. 
+
+## The (many) benefits of OS
+-**Efficiency:** promoting efficiency by reducing potential unnecessary research duplication and costs while promoting data reuse
+-**Transparency:** research can become more transparent by facilitating constructive feedback, adhering to FAIR (findable, accessible, interoperable and reusable) data principles, and fostering open access to knowledge.
+-**Data quality:** through reproducibility and increased data evaluation, quality is assured and issues like data manipulation are more easily addressed.
+-**Collaboration and innovation:** collaboration and innovation thrive with broader research access, hastened knowledge transfer.
+-**Societal advantage:** societal advantages include international collaboration and faster turn over of knowledge for addressing urgent issues such as ageing populations, aiding in more effective solution identification.
+
+See Chapter 9 for more information about methodology.
+
+## Possible hurdles, importance of guidance
+OS has two challenges that have been identified by its practitioners: Starting and the time cost.
+**1 - Starting**: A common struggle is the first step into OS. Many researchers are unsure on how to start implementing OS and what guidelines to follow. There is a lack of clear guidelines available and a large amount of conflicting information online.
+**2 - Time cost**: Writing a preregistration requires researchers to establish their entire analysis plan ahead of the actual analysis. This demands researchers to write out this plan with their hypothesis for confirmatory and exploratory analyses for confirmatory and exploratory analyses.  Additionally, time constraints consist of creating a file and documentation (e.g.: metadata, scripts, wet lab protocols) that can be read and understood by anyone in the field.
+
+See Chapter 2 for more information on preregistration. 
+
+## OS in neuroscience
+Neuroscience is an interdisciplinary domain where disciplines are integrated to investigate the complexity of the nervous system. Fields such as biology, computer science, engineering, psychology, physics and more are utilised to gain insight on its structure, function, development, genetics and pathology. The aim of neuroscience is to understand neural processes, neurological and psychiatric disorders, develop treatments and uncover the biological basis of consciousness, cognition, and human behaviour.
+
+The field of neuroscience is relatively young and is continuously expanding. The research has been generated at an exponential rate in the last 20 years, leading to an unprecedented amount of data in a short amount of time. This presents both opportunities and challenges. Given the complexity of studying the brain and its functions, embracing OS principles can aid to manage the expanding body of data and to accelerate progress in the field.
+
+See Chapter 3 for more information on data sharing in Neuroscience. 
+
+## Principal Investigators vs. Early-career Researchers
+We want this guidebook to serve as a vital resource for both Principal Investigators (PIs) and early career researchers. Early career researchers are more often aware of the significance of OS practices i.e. due to their exposure to OS during their schooling or at different research groups during internships. Some labs and PIs may benefit from further encouragement to adopt OS principles. PIs play a crucial role in fostering an OS culture by supporting and encouraging their PhD students and researchers. 
+
+## An OS guide in neuroscience for all
+This guidebook serves as a one-stop informative OS overview for everyone in the field. Everyone from principal investigators to early-career researchers can benefit from it. Whilst early-career researchers are often already more familiar with the concept and importance of OS, some principal investigators might need more convincing. The guidelines in this guidebook aim to assist individuals in embracing OS principles and practices, providing a clear starting point for implementation and encouraging widespread adoption throughout the research community.