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http://www.hedtags.org/

Strategy guide for HED Annotation. Framework for systematically describing laboratory and real world events.HED tags are comma separated path strings. Organized in forest of groups with roots Event, Item, Sensory presentation, Attribute, Action, Participant, Experiment context, and Paradigm. Used for preparing brain imaging data for automated analysis and meta analysis. Applied to brain imaging EEG, MEG, fNIRS, multimodal mobile brain or body imaging, ECG, EMG, GSR, or behavioral data. Part of Brain Imaging Data Structure standard for brain imaging.

Proper citation: HED tags (RRID:SCR_017630) Copy   

•   Source: SciCrunch Registry

https://github.com/PriceLab/TReNA

Methods for reconstructing transcriptional regulatory networks.

Proper citation: TReNA (RRID:SCR_017458) Copy   

•   Source: SciCrunch Registry

https://biccn.org

Consortium for the cell census in the brain. Integrated network of data generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate brains.

Proper citation: BICCN (RRID:SCR_015820) Copy   

•   Source: SciCrunch Registry

https://nemoarchive.org/

Data repository specifically focused on storage and dissemination of omic data generated from BRAIN Initiative and related brain research projects. Data repository and archive for BCDC and BICCN project, among others. NeMO data include genomic regions associated with brain abnormalities and disease, transcription factor binding sites and other regulatory elements, transcription activity, levels of cytosine modification, histone modification profiles and chromatin accessibility.

Proper citation: NeMOarchive (RRID:SCR_016152) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/psc/

Data analysis software that can simultaneously characterize a large number of white matter bundles within and across different subjects for group analysis. It has three major components: construction of the structural connectome for the whole brain, low-dimensional representation of streamlines in each connection, and multi-level connectome analysis.

Proper citation: Mapping Population-based Structural Connectomes (RRID:SCR_016232) Copy   

•   Source: SciCrunch Registry

https://www.nimhgenetics.org/

Stores biosamples, genetic, pedigree and clinical data collected in designated NIMH-funded human subject studies. RGR database likewise links to other repositories holding data from same subjects, including dbGAP, GEO and NDAR. Allows to access these data and biospecimens (e.g., lymphoblastoid cell lines, induced pluripotent cell lines, fibroblasts) and further expand genetic and molecular characterization of patient populations with severe mental illness.

Proper citation: NIMH Repository and Genomics Resources (NRGR) (RRID:SCR_016318) Copy   

•   Source: SciCrunch Registry

http://compbio.mit.edu/ChromHMM/

Software tool for chromatin state discovery and characterization. Used for chromatin state discovery and genome annotation of non coding genome using epigenomic information across one or multiple cell types. Combines multiple genome wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer complete annotation for each cell type. Provides automated enrichment analysis of resulting annotations.

Proper citation: ChromHMM (RRID:SCR_018141) Copy   

•   Source: SciCrunch Registry

https://health.uconn.edu/worm-lab/track-a-worm/

Open source system for quantitative assessment of C. Elegans locomotory and bending behavior. Used for quantitative behavioral analyses to understand circuit and gene bases of behavior. Constantly records and analyzes position and body shape of freely moving worm at high magnification.

Proper citation: Track-A-Worm (RRID:SCR_018299) Copy   

•   Source: SciCrunch Registry

https://www.epimodel.org/

Software R package for mathematical modeling of infectious disease over networks. Provides tools for simulating and analyzing mathematical models of infectious disease dynamics. Mathematical Modeling of Infectious Disease Dynamics.

Proper citation: EpiModel (RRID:SCR_018539) Copy   

•   Source: SciCrunch Registry

http://pinet-server.org

Web platform for downstream analysis and visualization of proteomics data. Server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. Primary input for server consists of set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values.

Proper citation: piNET (RRID:SCR_018693) Copy   

•   Source: SciCrunch Registry

https://github.com/aplbrain/saber

Library of containerized tools and workflow deployment system for enabling processing of large neuroimaging datasets. Provides canonical neuroimaging workflows specified in standard workflow language (CWL), integration with workflow execution engine (Airflow), imaging database (bossDB), and parameter database (Datajoint) to deploy workflows at scale, and tools to automate deployment and optimization of neuroimaging pipelines.

Proper citation: Scalable Analytics for Brain Exploration Research (RRID:SCR_018812) Copy   

•   Source: SciCrunch Registry

https://openwetware.org/wiki/RAVE

Open source software tool for reproducible analysis and visualization of intracranial EEG data. Used for analysis of intracranial electroencephalogram data, including data collected using strips and grids (electrocorticography, ECoG) and depth electrodes (stereotactic EEG).

Proper citation: RAVE (RRID:SCR_019040) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/CENlab/software.html

Stochastic reaction-diffusion simulator in Java which is used for simulating neuronal signaling pathways.

Proper citation: NeuroRD (RRID:SCR_014769) Copy   

•   Source: SciCrunch Registry

https://github.com/r3fang/SnapATAC

Software package for analyzing scATAC-seq datasets.Used to dissects cellular heterogeneity in unbiased manner and map trajectories of cellular states. Can process data from up to million cells. Incorporates existing tools into comprehensive package for analyzing single cell ATAC-seq dataset.

Proper citation: SnapATAC (RRID:SCR_020981) Copy   

•   Source: SciCrunch Registry

http://cobre.mrn.org/megsim/

Realistic simulated MEG datasets ranging from basic sensory to oscillatory sets that mimic functional connectivity; as well as basic visual, auditory, and somatosensory empirical sets. The simulated sets were created for the purpose of testing analysis algorithms across the different MEG systems when the truth is known. MEG baseline recordings were obtained from 5 healthy participants, using three MEG systems: VSM/CTF Omega, Elekta Neuromag Vectorview, 4-D Magnes 3600. Simulated signals were embedded within the CTF and Neuromag 306 baseline recordings (4-D to be added). Participant MRIs are available. Averaged simulation files are available as netcdf files. Neuromag 306 averaged simulations are also available in fif format. Also available: single trials of data where the simulated signal is jittered about a mean value, continuous fif files where the simulated signal is marked by a trigger, and simulations with oscillations added to mimic functional connectivity.

Proper citation: MEGSIM (RRID:SCR_002420) Copy   

•   Source: SciCrunch Registry

http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

•   Source: SciCrunch Registry

http://www.armystarrs.org/

Study of mental health risk and resilience factors ever conducted among military personnel. The purpose of Army STARRS is to identify as quickly as possible factors that protect or pose risks to Soldiers'' emotional well-being and overall mental health so that the Army may apply the knowledge to its ongoing health promotion, risk reduction, and suicide prevention efforts. Army STARRS investigators will use four separate study components the Historical Data Study, New Soldier Study, All Army Study, and Soldier Health Outcomes Study to identify factors that help protect a Soldier''s mental health and factors that put a Soldier''s mental health at risk. Army STARRS is a five-year study that will run through 2014. Findings will be reported as they become available, so that the Army may apply them to its ongoing health promotion, risk reduction, and suicide prevention efforts. Given its length and scope, Army STARRS will generate a vast amount of information and will allow investigators to focus on periods in a military career that are known to be high risk for psychological problems. The information gathered from volunteer participants throughout the study will help researchers identify not only potentially relevant risk factors, but potential protective factors as well. Because promoting mental health and reducing suicide risk are important for all Americans, the findings from Army STARRS will benefit not only servicemembers but the nation as a whole. NIMH has assembled a group of renowned experts to carry out this research including teams from the Uniformed Services University of the Health Sciences (USUHS), the University of California, San Diego, University of Michigan, Harvard Medical School, and NIMH. Additional Army and NIMH program staff will contribute to the oversight and implementation of the study. This research team brings together international leaders in military health, health and behavior surveys, epidemiology, suicide, and genetic and neurobiological factors involved in psychological health.

Proper citation: Army STARRS (RRID:SCR_006708) Copy   

•   Source: SciCrunch Registry

http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/practical/step-bd/index.shtml

A long-term outpatient study designed to find out which treatments, or combinations of treatments, are most effective for treating episodes of depression and mania and for preventing recurrent episodes in people with bipolar disorder. This study has been completed. (2005) STEP-BD is evaluating all the best-practice treatment options used for bipolar disorder: mood-stabilizing medications, antidepressants, atypical antipsychotics, and psychosocial interventions - or talk therapies - including Cognitive Behavioral Therapy, Family-focused Therapy, Interpersonal and Social Rhythm Therapy, and Collaborative Care (psychoeducation). There are two kinds of treatment pathways in STEP-BD, and participants may have the opportunity to take part in both. The medications and psychosocial interventions provided in these pathways are considered among the best choices of treatment for bipolar disorder in everyday clinical practice. In the Best Practice Pathway, participants are followed by a STEP-BD certified doctor and all treatment choices are individualized. Everyone enrolled in STEP-BD may participate in this pathway. Participants and their doctors work together to decide on the best treatment plans and to change these plans if needed. Also, anyone who wishes to stay on his or her current treatment upon entering STEP-BD may do so in this pathway. Adolescents and adults age 15 years and older may participate in the Best Practice Pathway. For adults age 18 and older, another way to participate is in the STEP-BD Randomized Care Pathways. Depending on their symptoms, participants may be offered treatment in one or more of these pathways during the course of the study. The participants remain on mood-stabilizing medication. However, because doctors are uncertain which of several treatment strategies work best for bipolar disorder, another medication and/or talk therapy may be added. Each Randomized Care Pathway involves a different set of these additional treatments. Unlike in the Best Practice Pathway, the participants in the Randomized Care Pathways are randomly assigned to treatments. Also, in some cases, neither the participant nor the doctor will be told which of the different medications is being added. This is called a double-blind study and is done so that the medication effects can be evaluated objectively, without any unintended bias that may come from knowing what has been assigned. Participants will not be assigned medications that they have had bad reactions to in the past, that they are strongly opposed to, or that the doctor feels are unsuitable for them. The medication(s) participants may be randomly assigned to in the Randomized Care Pathways are free of charge. There are other treatment options for participants if they do not respond well to the treatment assigned to them. Also, participants may return to the Best Practice Pathway at any time. About 1,500 individuals will be enrolled in at least one Randomized Care Pathway during their period of participation in STEP-BD. It is important to note that STEP-BD provides continuity of care. For example, if a participant starts out in the Best Practice Pathway and later chooses to enter one of the Randomized Care Pathways, he or she continues with the same STEP-BD doctor and treatment team. Then, after completing the Randomized Care Pathway, the participant may return to the Best Practice Pathway for ongoing, individually-tailored treatment. Follow the link to view study info at Clinicaltrials.gov, http://www.clinicaltrials.gov/ct/show/NCT00012558?order=1

Proper citation: Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) (RRID:SCR_008844) Copy   

•   Source: SciCrunch Registry

http://research.mssm.edu/cnic/

Center to advance research and training in mathematical, computational and modern imaging approaches to understanding the brain and its functions. Software tools and associated reconstruction data produced in the center are available. Researchers study the relationships between neural function and structure at levels ranging from the molecular and cellular, through network organization of the brain. This involves the development of new computational and analytic tools for imaging and visualization of 3-D neural morphology, from the gross topologic characteristics of the dendritic arbor to the fine structure of spines and their synapses. Numerical simulations of neural mechanisms based on these structural data are compared with in-vivo and in-vitro electrophysiological recordings. The group also develops new theoretical and analytic approaches to exploring the function of neural models of working memory. The goal of this analytic work is to combine biophysically realistic models and simulations with reduced mathematical models that capture essential dynamical behaviors while reproducing the functionally important features of experimental data. Research areas include: Imaging Studies, Volume Integration, Visualization Techniques, Medial Axis Extraction, Spine Detection and Classification, Applications of Rayburst, Analysis of Spatially Complex Structures, Computational Modeling, Mathematical and Analytic Studies

Proper citation: Computational Neurobiology and Imaging Center (RRID:SCR_013317) Copy   

•   Source: SciCrunch Registry

http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/datasets/nimh-procedures-for-requesting-data-sets.shtml

A listing of data sets from NIMH-supported clinical trials. Limited Access Datasets are available from numerous NIMH studies. NIMH requires all investigators seeking access to data from NIMH-supported trials held by NIMH to execute and submit as their request the appropriate Data Use Certification pertaining to the trial. The datasets distributed by NIMH are referred to as limited access datasets because access is limited to qualified researchers who complete Data Use Certifications.

Proper citation: Limited Access Datasets From NIMH Clinical Trials (RRID:SCR_005614) Copy   

•   Source: SciCrunch Registry