SORTED
SORTED: A curated collection of interesting ideas, tools, and resources in neuroscience, data management, and data science, all in the spirit of Open Science. Additionally, it includes interesting miscellaneous links related to AI tools and productivity.
https://github.com/PTDZ/SORTED
Last synced: 3 days ago
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Reading corner
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Articles
- Paper
- Paper
- Paper - 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"
- Article
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
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- Paper - 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"
- Paper
- Paper
- Article
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Paper - 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"
- Article
- Paper
- Paper
- Article
- Paper
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- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
- Article
- Paper
- Paper
- Paper - 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"
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Programming
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Other
- Karty Data Science
- Paper - 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"
- Paper - 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"
- Paper - 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"
- Paper - 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"
- Paper - 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"
- Paper - 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"
- Paper - 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"
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Unreliable Science (*and how to try overcome this issue)
- Paper - but-bogus effects
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- Paper - 9)
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- Paper - positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates."
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- Article
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- Paper - wide attention through gradual processes of diffusion."
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Other articles
- Paper - 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"
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Design
- The Design of Everyday Things - friendly.
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Relaxing on a hammock under a tree...
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Open data movement
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Platforms
- OpenNeuro - compliant MRI, PET, MEG, EEG, and iEEG data
- OpenNeuro PET - compliant PET data
- NeuroVault
- BossDB
- GigaDB
- Machine learning datasets
- GIN
- NeMO - 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
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Journals
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Other lists
- Wikipedia: List of neuroscience databases
- Most Cited Deep Learning Papers - Most Cited Deep Learning Papers
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Tools
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General: research
- BIDS
- Protocols.io
- ADDI - 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"
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Coding/ Software
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Neuroscience
- Neurosynth - scale, automated synthesis of functional magnetic resonance imaging (fMRI) data
- NEMAR
- BrainIAK
- ENIGMA toolbox - and macroscale data, Multiscale analytical workflows, 100+ ENIGMA-derived statistical maps
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AI: writing/ information searching
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Miscellaneous for Researchers
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Neuroscience
- SPAMRI - Based Processing and Analysis of Magnetic Resonance Imaging
- Neurosynth - scale, automated synthesis of functional magnetic resonance imaging (fMRI) data.
- ReproNim
- NiMARE - analyses (Neuroimaging Meta-Analysis Research Environment).
- BrainMap - based results (x,y,z) in Talairach or MNI space.
- fMRIPrep - based and resting-state fMRI data.
- BrainAge - cole/brainageR): Brain-age models (brain-predicted age value from a raw T1-weighted MRI scans).
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Learning
- ReproNim Statistics Module - values and their issues / Statistical power in neuroimaging and statistical reproducibility / The positive Predictive Value / Cultural and psychological issues
- Seeing Theory
- ReproNim Statistics Module - values and their issues / Statistical power in neuroimaging and statistical reproducibility / The positive Predictive Value / Cultural and psychological issues
- Neuroscience Tutorials
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General tools
- Sample Consent Forms for neuroimaging research
- Journal Citation Reports - neutral data".
- Most Wiedzy/ Bridge of Knowledge - 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).
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Fellowships & Grants
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Initiatives, research groups, associations, labs, companies
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Predatory journals/publishers etc.
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Summer Schools
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Data visualisation
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AI: writing/ information searching
- BrainPainter
- HSLuv - friendly alternative to HSL
- Information is beautiful
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Publishing
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Predatory journals/publishers etc.
- Think-Check-Submit - sector initiative aims to educate researchers, promote integrity, and build trust in credible research and publications
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Summer Schools / Courses / Hackathons etc.
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Other
- Neurohackademy
- Neuromatch - week program)) & Neuromatch Conference (a conference for the computational neuroscience community)
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AI tools
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Predatory journals/publishers etc.
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Conferences / Symposia
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Other
- AI in aging and age-related diseases - 13 November 2022, on-line and on-demand; deadline: 3 October 2022
- NEURONUS Neuroscience Forum - 17.10.2022)
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Initiatives, research groups, associations...
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Other
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Interesting companies & labs, internships, job advertisements, grants & travel grants
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Biohacking
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Predatory journals/publishers etc.
- Biohacking Brain Health
- Bulletproof Blog
- SelfHacked - based information on health, biohacking, and self-improvement.
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Miscellaneous
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Other
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Additional Resources:
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Journals (for Data Notes)
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Open Data Science
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Data Repositories (suitable for neuroimaging datasets)
- GigaDB
- Zenodo - purpose open data repository.
- NeuroVault
- GIN
- DABI
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Journals (for Data Notes)
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AI tools for researchers
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Predatory journals/publishers etc.
- Perplexity - to-date information by combining LLMs with real-time web access.
- Litmaps
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Productivity
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Predatory journals/publishers etc.
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Data Visualisation
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Predatory journals/publishers etc.
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Categories
Reading corner
164
Miscellaneous for Researchers
22
Open data movement
11
Tools
10
Open Data Science
9
Initiatives, research groups, associations, labs, companies
6
Interesting companies & labs, internships, job advertisements, grants & travel grants
6
Additional Resources:
4
Biohacking
3
Data visualisation
3
Summer Schools / Courses / Hackathons etc.
2
Initiatives, research groups, associations...
2
AI tools for researchers
2
Conferences / Symposia
2
Productivity
1
AI tools
1
Miscellaneous
1
Data Visualisation
1
Publishing
1
Sub Categories
Articles
113
Unreliable Science (*and how to try overcome this issue)
39
Other
21
Neuroscience
11
Predatory journals/publishers etc.
10
Fellowships & Grants
8
Platforms
8
Journals (for Data Notes)
8
Data Repositories (suitable for neuroimaging datasets)
5
AI: writing/ information searching
5
Summer Schools
5
Learning
4
General: research
3
General tools
3
Other lists
2
Design
1
Other articles
1
Programming
1
Coding/ Software
1
Relaxing on a hammock under a tree...
1
Journals
1
Keywords
awesome
4
awesome-list
4
neuroscience
3
computational-neuroscience
2
brain
2
open-science
2
list
2
remarkable-tablet
1
python
1
nirs
1
mooc
1
meg
1
matlab
1
intelligence
1
eeg
1
machine-learning
1
deep-neural-networks
1
deep-learning
1
tutorials
1
resources
1
quickstart
1
collection
1
science
1
open-datasets
1
open-data
1
computational-science
1
simulation-neuroscience
1
neuroscience-methods
1
neuroscience-data
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neuron-models
1
neuroimaging-data
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neural-simulators
1
neural-simulations
1
electrophysiological-data
1
computational-biology
1