SORTED: a curated list of interesting science ideas, tools and links (cognitive/neuro & data science)
Sites that allow to share neuroimaging datasets.
- OpenNeuro: BIDS-compliant MRI, PET, MEG, EEG, and iEEG data
- OpenNeuro PET: BIDS-compliant PET data
- NeuroVault: unthresholded statistical maps, parcellations, and atlases produced by MRI and PET studies
- BossDB: a volumetric database for 3D and 4D neuroscience data
- GigaDB: GigaDB contains 2201 discoverable, trackable, and citable datasets that have been assigned DOIs and are available for public download and use
- Machine learning datasets: a list of machine learning datasets from across the web
- Harvard Dataverse:"Harvard Dataverse is a repository for research data.
- Dryad: Dryad is an open data publishing platform and a community committed to the open availability and routine re-use of all research data.
- GIN: Modern Research Data Management for Neuroscience
- Zenodo: Open data publishing platform
- NeMO: The Neuroscience Multi-omic Archive (NeMO Archive) is a data repository specifically focused on the storage and dissemination of omic data generated from the BRAIN Initiative, SCORCH consortium and related brain research projects
- DABI: Data Archive BRAIN Initiative, a shared repository for invasive neurophysiology data from the NIH Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative
- openlists/ElectrophysiologyData: EEG, MEG, ECoG/iEEG, and LFP data
- meagmohit/EEG-Datasets: EEG
- Wikipedia: List of neuroscience databases
- Most Cited Deep Learning Papers: Awesome - Most Cited Deep Learning Papers
- BIDS: Brain Imaging Data Structure; simple and intuitive way to organize and describe neuroimaging and behavioral data
- Protocols.io: science methods, assays, clinical trials, operational procedures and checklists for keeping your protocols up do date as recommended by Good Laboratory Practice (GLP) and Good Manufacturing Practice (GMP)
- Sample Consent Forms for neuroimaging research (EN/DE) | The Open Brain Consent: an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools
- DataLad: a free and open source distributed data management system
- ADDI + AD Workbench: Alzheimer's Disease Data Initiative; "a free data sharing platform, data science tools, funding opportunities, and global collaborations, ADDI is advancing scientific breakthroughs and accelerating progress towards new treatments and cures for AD and related dementias"
- The Human Protein Atlas: "The Human Protein Atlas is a Swedish-based program initiated in 2003 with the aim to map all the human proteins in cells, tissues, and organs using an integration of various omics technologies, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics, and systems biology."
- Most Wiedzy/ Bridge of Knowledge: a Polish-based system with collection of publications, studies, projects and a lot of other types of resources from a number of different subject areas (open-access)
- Journal Citation Reports: "the world's leading journals and publisher-neutral data"
- Dask: parallel computing with python
- Docker: OS-level virtualization to deliver software in packages called containers
- Statistics in R: guidelines
- fMRIPrep: a preprocessing pipeline for task-based and resting-state fMRI data
- Neurosynth: a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data
- BrainMap: a database of published functional and structural neuroimaging experiments with coordinate-based results (x,y,z) in Talairach or MNI space
- NEMAR: an open access data, tools, and compute resource for assessing and processing human NeuroElectroMagnetic data shared by its authors thru OpenNeuro
- ReproNim: ReproNim delivers a reproducible analysis framework
- NiMARE: a Python package for neuroimaging meta-analyses (Neuroimaging Meta-Analysis Research Environment)
- BrainIAK: advanced fMRI analyses in Python, optimized for speed under the hood with MPI, Cython, and C++
- Brain-age models (brain-predicted age value from a raw T1-weighted MRI scans): BrainAge, brainageR
- SPAMRI: A MATLAB Toolbox for Surface-Based Processing and Analysis of Magnetic Resonance Imaging
- ENIGMA toolbox: Python/MATLAB based. Cortical and subcortical visualization tools, Preprocessed micro- and macroscale data, Multiscale analytical workflows, 100+ ENIGMA-derived statistical maps
- Chat GPT
- ParaphrasingTool
- Smodin: AI content detector
- Elicit: AI research assistant
- BrainPainter: a free software for visualisation of brain structures, biomarkers and associated pathological processes
- HSLuv: HSLuv is a human-friendly alternative to HSL
- Information is beautiful: The Information is Beautiful Awards celebrates excellence & beauty in data visualization, infographics, interactives & information art
- Think-Check-Submit: this international, cross-sector initiative aims to educate researchers, promote integrity, and build trust in credible research and publications
- Beall's List: expanded 2022: a list of predatory journals & trusted resources
- The Design of Everyday Things (Donald Norman): how users use objects, and how to optimize and standardize things and make them more intuitive and user-friendly
- The Art of Readable Code (Dustin Boswell & Trevor Foucher): a basic principles and practical techniques that one can apply to write a better code
- What If? Serious Scientific Answers to Absurd Hypothetical Questions (Randall Munroe)
- Why Most Published Research Findings Are False (PLOS/ John Ioannidis) | Paper / Wiki: an essay written by John Ioannidis (Stanford School of Medicine); author argues that a large number of papers in medical research contain results that in fact cannot be replicated and are a false positive results
- How scientists fool themselves – and how they can stop (Nature/ Regina Nuzzo) | Article / PDF: cognitive fallacies in research and debiasing techniques
- Power failure: why small sample size undermines the reliability of neuroscience (Nature/ Katherine S. Button et al.) | Paper: low statistical power and its influence on true/false effects
- Scanning the horizon: towards transparent and reproducible neuroimaging research (Nat Rev Neurosci/ Russell A. Poldrack et al.) | Paper: problems that should be acknowledge during neuroimaging data analysis (low statistical power, flexibility in data analysis, software errors etc.)
- Variability in the analysis of a single neuroimaging dataset by many teams (Nature/ Rotem Botvinik-Nezer et al.) | Paper: analytical flexibility can have substantial effects on scientific conclusions
- How to get statistically significant effects in any ERP experiment (and why you shouldn't) (Psychophysiology/ Steven J. Luck & Nicholas Gaspelin) | Paper: the purpose of this paper is to demonstrate how common and seemingly innocuous methods for quantifying and analyzing ERP effects can lead to very high rates of significant-but-bogus effects
- Slowed canonical progress in large fields of science (PNAS/ Johan S. G. Chu & James A. Evans) | Paper: "Examining 1.8 billion citations among 90 million papers across 241 subjects, we find a deluge of papers does not lead to turnover of central ideas in a field, but rather to ossification of canon. Scholars in fields where many papers are published annually face difficulty getting published, read, and cited unless their work references already widely cited articles. New papers containing potentially important contributions cannot garner field-wide attention through gradual processes of diffusion."
- False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant (Joseph P. Simmons & Leif D. Nelson) | Paper: "First, we show that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates."
- Revised standards for statistical evidence (PNAS/ Valen E. Johnson) | Paper: "The lack of reproducibility of scientific research undermines public confidence in science and leads to the misuse of resources when researchers attempt to replicate and extend fallacious research findings. (...) Modifications of common standards of evidence are proposed to reduce the rate of nonreproducibility of scientific research by a factor of 5 or greater."
- The file drawer problem and tolerance for null results (Robert Rosenthal) | Paper: "For any given research area, one cannot tell how many studies have been conducted but never reported. The extreme view of the "file drawer problem" is that journals are filled with the 5% of the studies that show Type I errors, while the file drawers are filled with the 95% of the studies that show nonsignificant results."
- Is there a large sample size problem? (Richard A. Armstrong) | Paper & The paradox of large samples (S. Kunte & A. P. Gore) Paper: statistical issues with large sample sizes
- Do we really measure what we think we are measuring? (Dario Gordillo et al.) | Paper
- A hitchhiker’s guide to working with large, open-source neuroimaging datasets (Corey Horien et al.) | Paper: "Here we offer practical tips for working with large datasets from the end-user’s perspective. We cover all aspects of the data lifecycle: from what to consider when downloading and storing the data to tips on how to become acquainted with a dataset one did not collect and what to share when communicating results"
- Karty Data Science: [PL only]
- Neurohackademy: a summer school in neuroiming & data science, held at the University of Washington eScience Institute
- Neuromatch: Neuromatch Academy (computational techniques crucial both in academia and industry (3-week program)) & Neuromatch Conference (a conference for the computational neuroscience community)
- Google Summer of Code: a global, online program focused on bringing new contributors into open source software development
- neuroSummerSchools: list of summer schools in neuroscience and related fields
- ReproNim Statistics Module: Statistical basis for neuroimaging analyses: the basics / Effect size and variation of effect sizes in brain imaging / P-values and their issues / Statistical power in neuroimaging and statistical reproducibility / The positive Predictive Value / Cultural and psychological issues
- DataCamp
- Seeing Theory: a simple introduction to statistics and probability through the use of interactive visualizations (Brown University)
- AI in aging and age-related diseases: 11-13 November 2022, on-line and on-demand; deadline: 3 October 2022
- NEURONUS Neuroscience Forum: organized yearly in Kraków, Poland (2022: 15-17.10.2022)
- ENIGMA: The ENIGMA Consortium brings together researchers in imaging genomics to understand brain structure, function, and disease, based on brain imaging and genetic data
- The EuroLaD-EEG consortium: towards a global EEG platform for dementia (*more information is not yet available)
- BrainArt SIG: "The scope of the Brain–Art SIG is to promote the exchange between Art & Science by fostering the dialogue between artists and members of the OHBM community." | Paper
- DeepMind: "We’re a team of scientists, engineers, ethicists and more, committed to solving intelligence, to advance science and benefit humanity"
- The Center for Brains, Minds and Machines
- NeuroDataScience - ORIGAMI lab
- Opium: Polish National Institute for Machine Learning
- Travelling Fellowships, The Company of Biologists
- Bekker NAWA: [only PL]
- ReproNim/INCF Training Fellowship
- Neuroscience related tutorials
- Awesome Neuroscience: "A curated list of awesome neuroscience libraries, software and any content related to the domain." (*more general/cell-level oriented list)
- Open Computational Neuroscience Resources: a publicly-editable collection of open computational neuroscience resources
- Open Science Resources: a publicly-editable collection of open science resources, including tools, datasets, meta-resources, etc.
- OtherLists: a list of other lists that collect & curate resources
- eDoktorant: a website for PhD students (only PL)
- reMarkable list: a curated list of projects related to the reMarkable tablet
- AI tools and applications
Patrycja | mail[at]ptdz.pl
https://github.com/PTDZ/SORTED
Feel free to edit and create a pull request!