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https://cran.r-project.org/web/packages/MetaCycle/vignettes/implementation.html

Software R package for detecting rhythmic signals from large scale time-series data. Used to evaluate periodicity in large scale data.

Proper citation: MetaCycle (RRID:SCR_025729) Copy   

•   Source: SciCrunch Registry

https://www.c4r-nih.org

Portal provides information about nationwide study of more than 50,000 individuals to determine factors that predict disease severity and long-term health impacts of COVID-19.

Proper citation: Collaborative Cohort of Cohorts for COVID-19 Research (RRID:SCR_026322) Copy   

•   Source: SciCrunch Registry

https://github.com/tomcatsmith19/ArucoDetection

Automated rodent behavioral scoring system, complete with 3D design files and code/software. System monitors behavioral engagement using open-source software. 3D design files and necessary software has been made available, as well as code that can be used for data analysis.

Proper citation: ArUco (RRID:SCR_026572) Copy   

•   Source: SciCrunch Registry

https://github.com/Washington-University/HCPpipelines

Software package as set of tools, primarily shell scripts, for processing multi-modal, high-quality MRI images for the Human Connectome Project. Minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space.

Proper citation: HCP Pipelines (RRID:SCR_026575) Copy   

•   Source: SciCrunch Registry

https://endomap.hms.harvard.edu/

Structural interactome viewer. Interactive database of endosomal protein-protein interactions identified by cross-linking mass spectrometry and modeled by AlphaFold multimer. Structural protein interactome of human early endosomes.

Proper citation: EndoMap (RRID:SCR_026690) Copy   

•   Source: SciCrunch Registry

https://github.com/Yonghao-Holden/TEProf3

Software pipeline to detect Transposable Elements transcripts. Used to identify TE-derived promoters and transcripts using transcriptomic data from multiple sources, including short-read RNA-seq data, long-read RNA-seq data and single cell RNA-seq data.

Proper citation: TEProf3 (RRID:SCR_027288) Copy   

•   Source: SciCrunch Registry

https://github.com/smorabit/hdWGCNA

Software R package for performing weighted gene co-expression network analysis in high dimensional transcriptomics data such as single-cell RNA-seq or spatial transcriptomics.

Proper citation: hdWGCNA (RRID:SCR_027496) Copy   

•   Source: SciCrunch Registry

https://github.com/ReproBrainChart

Open data resource for mapping brain development and its associations with mental health. Integrates data from 5 large studies of brain development in youth from three continents (N = 6,346). Bifactor models were used to create harmonized psychiatric phenotypes, capturing major dimensions of psychopathology. Neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner.

Proper citation: Reproducible Brain Charts (RRID:SCR_027837) Copy   

•   Source: SciCrunch Registry

https://doi.org/10.17605/OSF.IO/WDR78

Open source resource of manually curated and expert reviewed infant brain segmentations hosted on OpenNeuro.org. and OSF.io. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1–9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Proper citation: Baby Open Brains (RRID:SCR_027836) Copy   

•   Source: SciCrunch Registry

https://neurojson.io/

Web application for human-readable, searchable neuroimaging datasets using universally accessible JSON format and URL-based RESTful APIs. NeuroJSON.io is built upon highly scalable document-store NoSQL database technologies, specifically, open-source Apache CouchDB engine, that can handle millions of datasets without major performance penalties. Provides fine-grained data search capabilities to allow users to find, preview and re-combine complex data records from public datasets before download.

Proper citation: NeuroJSON.io (RRID:SCR_027793) Copy   

•   Source: SciCrunch Registry

https://www.c4r.io/

United States based educational platform dedicated to changing research culture by providing comprehensive educational materials, promoting best practices, and fostering a community committed to improving research reliability. Helps researchers learn and implement scientific rigor, such as proper experimental design, avoiding bias, and statistical analysis. Offers modules that address common issues such as experimental design, causal inference, avoiding statistical fallacies, and writing transparently reproducible code.

Proper citation: C4R (RRID:SCR_028702) Copy   

•   Source: SciCrunch Registry

https://github.com/pacificbiosciences/trgt

Software tool for plotting pureTarget repeat panel results (generates waterfall plot). Used for Tandem repeat genotyping and visualization from PacBio HiFi data.

Proper citation: TRGT (RRID:SCR_028810) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/ir/

Data repository for neuroimaging data in DlCOM and NIFTI formats. It allows users to search for and freely download publicly available data sets relating to normal subjects and those with diagnoses such as: schizophrenia, ADHD, autism, and Parkinson's disease.XNAT-based image registry that supports both NIfTI and DICOM images to promote re-use and integration of NIH funded data.

Proper citation: NITRC-IR (RRID:SCR_004162) Copy   

•   Source: SciCrunch Registry

https://nda.nih.gov/

The National Institute of Mental Health Data Archive (NDA) makes available human subjects data collected from hundreds of research projects across many scientific domains. Research data repository for data sharing and collaboration among investigators. Used to accelerate scientific discovery through data sharing across all of mental health and other research communities, data harmonization and reporting of research results. Infrastructure created by National Database for Autism Research (NDAR), Research Domain Criteria Database (RDoCdb), National Database for Clinical Trials related to Mental Illness (NDCT), and NIH Pediatric MRI Repository (PedsMRI).

Proper citation: NIMH Data Archive (RRID:SCR_004434) Copy   

•   Source: SciCrunch Registry

http://tbi.ci.uchicago.edu/

Project to define a roadmap for diffusion MR imaging of traumatic brain imaging and design an infrastructure to implement the recommendations and tested to ensure feasibility, disseminate results, and facilitate deployment and adoption. The research roadmap and infrastructure development will concentrate on three areas: 1) standardization of diffusion imaging methodology, 2) trial design and patient selection for acute or chronic therapy, and 3) development of multi-center collaborations and repositories for evaluating whether advanced diffusion imaging does improve decision making and TBI patients' outcomes. # DTI MRI reproducability: One of the major areas of investigation in this project is to study the reproducibility of data acquisition and image analysis algorithms. Understanding reproducibility defines a base level of deviation from which scans can be analyzed with statistical significance. As part of this work they are also developing site qualification criteria with the intention of setting limits on the MR system minimal performance for acceptable use in TBI evaluation. # Infrastructure for image storage, analysis and visualization: There is a continuing need to refine and extend software methods for diffusion MRI data analysis and visualization. Not only to translate tools into clinical practice, but also to encourage continuation of the innovation and development of new tools and techniques. To deliver upon these goals they are designing and implementing a storage and computational infrastructure to provide access to shared datasets and intuitive interfaces for analysis and visualization through a variety of tools. A strong emphasis has been placed on providing secure data sharing and the ability to add community defined common data elements. The infrastructure is built upon a Software-as-a-Service model, in which tools are hosted and managed remotely allowing users access through well-defined interfaces. The final service will also facilitate composition or orchestration of workflows composed of different analysis and processing tasks (for example using LONI or XNAT pipelines) with the ultimate goal of providing automated no-click evaluations of diffusion MRI data. # Tool development: The final aspect of this project aims to facilitate and encourage tool development and contribution. By providing access to open datasets, they will create a platform on which tool developers can compare and improve and their tools. When tools are sufficiently mature they can be exposed in the infrastructure mentioned above and used by researchers and other developers.

Proper citation: Diffusion MRI of Traumatic Brain Injury (RRID:SCR_001637) Copy   

•   Source: SciCrunch Registry

http://neuromorpho.org/index.jsp

Centrally curated inventory of digitally reconstructed neurons associated with peer-reviewed publications that contains some of the most complete axonal arborizations digitally available in the community. Each neuron is represented by a unique identifier, general information (metadata), the original and standardized ASCII files of the digital morphological reconstruction, and a set of morphometric features. It contains contributions from over 100 laboratories worldwide and is continuously updated as new morphological reconstructions are collected, published, and shared. Users may browse by species, brain region, cell type or lab name. Users can also download morphological reconstructions for research and analysis. Deposition and distribution of reconstruction files ultimately prevents data loss. Centralized curation and annotation aims at minimizing the effort required by data owners while ensuring a unified format. It also provides a one-stop entry point for all available reconstructions, thus maximizing data visibility and impact.

Proper citation: NeuroMorpho.Org (RRID:SCR_002145) Copy   

•   Source: SciCrunch Registry

http://www.gensat.org/

Gene expression data and maps of mouse central nervous system. Gene expression atlas of developing adult central nervous system in mouse, using in situ hybridization and transgenic mouse techniques. Collection of pictorial gene expression maps of brain and spinal cord of mouse. Provides tools to catalog, map, and electrophysiologically record individual cells. Application of Cre recombinase technologies allows for cell-specific gene manipulation. Transgenic mice created by this project are available to scientific community.

Proper citation: Gene Expression Nervous System Atlas (RRID:SCR_002721) Copy   

•   Source: SciCrunch Registry

http://kesm.cs.tamu.edu

A web-based, light-weight 3D volume viewer that serves large volumes (typically the whole brain) of high-resolution mouse brain images (~1.5 TB per brain, ~1 um resolution) from the Knife-Edge Scanning Microscope (KESM), invented by Bruce H. McCormick. Currently, KESMBA serves the following data sets: * Mouse: Whole-brain-scale Golgi (acquired 2008 spring): neuronal morphology: Choe et al. (2009) * Mouse: Whole-brain India Ink (acquired 2008 spring): vascular network: Choe et al. (2009); Mayerich et al. (2011); * Mouse: Whole-brain Golgi (acquired 2011 summer): neuronal morphology: Choe et al. (2011); Chung et al. (2011); * Mouse: Whole-brain Nissl (acquired 2009-2010 winter): somata (Choe et al. 2010) (Coming soon) They will ship you the full data set on a hard drive if you provide them with the hard drive and shipping cost.

Proper citation: KESM brain atlas (RRID:SCR_001559) Copy   

•   Source: SciCrunch Registry

http://www.pd-doc.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on December 02, 2011. Notice: This domain name expired on 10/29/11 and is pending renewal or deletion PD-DOC is a portal and a database resource, hosting a database and linking to other databases and data sets of clinical and translational data. PD-DOC functions to organize and facilitate clinical and translational research in Parkinson's disease. The PD-DOC Database contains standardized data collected by user institutions on large numbers of patients with Parkinsons disease and other parkinsonian disorders. In some cases, data is obtained at a single point in time, while in others data is collected repeatedly over time. The PD-DOC Database is composed of the Core Data Set (CDS) which consists of those variables required to be gathered for each subject whose data is entered into the PD-DOC database. In 2005, working groups of Udall Center and invited experts deliberated to establish the components of each CDS section (e.g. General Clinical, Cognitive/Behavioral, Postmortem Brain Neuropathological Findings). The PD-DOC CDS was established and designed to optimize data analyses and data mining for large numbers of subjects participating in a variety of research studies. In most cases corresponding DNA samples are available form the NINDS Human Genetic Repository (at Coriell). Much of the website is publicly available for viewing. To request access to sections of the website dealing with downloading or requesting data, requesting a consultation, or submitting data or other information you will need to register. Before registering, you should read the PD-DOC Policies. Note that PD-DOC data can be used for research purposes only. Once your registration is successfully completed you will be automatically logged into the website.

Proper citation: PD-DOC (RRID:SCR_001596) Copy   

•   Source: SciCrunch Registry

http://neurogeometry.net/

Open source software for automated and manual tracing of neurites from light microscopy stacks of images. NCTracer 2.0 is developed for the Windows 7, 64-bit operating system and requires a minimum of 4 GB of RAM. This version does not run on 32-bit computers, Mac or Linux OS.

Proper citation: Neural Circuit Tracer (RRID:SCR_000116) Copy   

•   Source: SciCrunch Registry