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http://www.informatics.jax.org

International database for laboratory mouse. Data offered by The Jackson Laboratory includes information on integrated genetic, genomic, and biological data. MGI creates and maintains integrated representation of mouse genetic, genomic, expression, and phenotype data and develops reference data set and consensus data views, synthesizes comparative genomic data between mouse and other mammals, maintains set of links and collaborations with other bioinformatics resources, develops and supports analysis and data submission tools, and provides technical support for database users. Projects contributing to this resource are: Mouse Genome Database (MGD) Project, Gene Expression Database (GXD) Project, Mouse Tumor Biology (MTB) Database Project, Gene Ontology (GO) Project at MGI, and MouseCyc Project at MGI.

Proper citation: Mouse Genome Informatics (MGI) (RRID:SCR_006460) Copy   

•   Source: SciCrunch Registry

http://research.nhgri.nih.gov/CGD/

Manually curated database of all conditions with known genetic causes, focusing on medically significant genetic data with available interventions. Includes gene symbol, conditions, allelic conditions, inheritance, age in which interventions are indicated, clinical categorization, and general description of interventions/rationale. Contents are intended to describe types of interventions that might be considered. Includes only single gene alterations and does not include genetic associations or susceptibility factors related to more complex diseases.

Proper citation: Clinical Genomic Database (RRID:SCR_006427) Copy   

•   Source: SciCrunch Registry

http://omia.angis.org.au/

Describes phenotype relationships with between breeds and genes. Catalogue/compendium of inherited disorders, other (single-locus) traits, and genes in 245 animal species. Database of genes, inherited disorders and traits in animal species other than human, mouse, and rats. Database contains textual information and references, as well as links to relevant records from OMIM, PubMed and Gene.

Proper citation: OMIA - Online Mendelian Inheritance in Animals (RRID:SCR_006436) Copy   

•   Source: SciCrunch Registry

http://www.physionet.org/physiobank/database/gaitndd/

Database of records from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), or amyotrophic lateral sclerosis (n = 13). Records from 16 healthy control subjects are also included here. The raw data were obtained using force-sensitive resistors, with the output roughly proportional to the force under the foot. Stride-to-stride measures of footfall contact times were derived from these signals.

Proper citation: Gait Dynamics in Neuro-Degenerative Disease Data Base (RRID:SCR_006979) Copy   

•   Source: SciCrunch Registry

http://blog.f1000.com/

A blog presented by Faculty of 1000 highlighting and linking to the latest, greatest research recommended by F1000. Contributors include F1000 staff, freelance journalists, and scientists. We encourage readers to participate in the conversation via email to suggest topics and contribute guest posts.

Proper citation: Naturally Selected (RRID:SCR_006572) Copy   

•   Source: SciCrunch Registry

http://www.physionet.org/physiobank/database/gaitpdb/

Database that contains measures of gait from 93 patients with idiopathic PD (mean age: 66.3 years; 63% men), and 73 healthy controls (mean age: 66.3 years; 55% men). The database includes the vertical ground reaction force records of subjects as they walked at their usual, self-selected pace for approximately 2 minutes on level ground. Underneath each foot were 8 sensors (Ultraflex Computer Dyno Graphy, Infotronic Inc.) that measure force (in Newtons) as a function of time. The output of each of these 16 sensors has been digitized and recorded at 100 samples per second, and the records also include two signals that reflect the sum of the 8 sensor outputs for each foot. This database also includes demographic information, measures of disease severity (i.e., using the Hoehn & Yahr staging and/or the Unified Parkinson's Disease Rating Scale) and other related measures (available in HTML or xls spreadsheet format). A subset of the database includes measures recorded as subjects performed a second task (serial 7 subtractions) while walking, which shows excerpts of swing time series from a patient with PD and a control subject, under usual walking conditions and when performing serial 7 subtractions. Under usual walking conditions, variability is larger in the patient with PD (Coefficient of Variation = 2.7%), compared to the control subject (CV = 1.3%). Variability increases during dual tasking in the subject with PD (CV = 6.5%), but not in the control subject (CV = 1.2%).

Proper citation: Gait in Parkinson's Disease (RRID:SCR_006891) Copy   

•   Source: SciCrunch Registry

http://www.genedb.org/Homepage/Pfalciparum

Database of the most recent sequence updates and annotations for the P. falciparum genome. New annotations are constantly being added to keep up with published manuscripts and feedback from the Plasmodium research community. You may search by Protein Length, Molecular Mass, Gene Type, Date, Location, Protein Targeting, Transmembrane Helices, Product, GO, EC, Pfam ID, Curation and Comments, and Dbxrefs. BLAST and other tools are available. The P. falciparum 3D7 nuclear genome is 23.3 Mb in size, with a karyotype of 14 chromosomes. The G+C content is approximately 19%. The P. falciparum genome is undergoing re-annotation. This process started in October 2007 with a weeklong workshop co-organized by staff from the Wellcome Trust Sanger Intistute and the EuPathDB team. Ongoing curation and sequence checking is being carried out by the Pathogen Genomics group. Plasmodium falciparum is the most deadly of the five Plasmodium species that cause human malaria. Malaria has a massive impact on human health; it is the worlds second biggest killer after tuberculosis. Around 300 million clinical cases occur each year resulting in between 1.5 - 2.7 million deaths annually, the majority in sub-saharan Africa. It is estimated that 3,000 children under the age of five years fall victim to malaria each day. Around 40% of the worlds population are at risk. In collaboration with EuPathDB, genomic sequence data and annotations are regularly deposited on PlasmoDB where they can be integrated with other datasets and queried using customized queries.

Proper citation: GeneDB Pfalciparum (RRID:SCR_006567) Copy   

•   Source: SciCrunch Registry

http://neuropathologyblog.blogspot.com/

Blog by Brian E. Moore, MD, discussing issues pertaining to the practice of neuropathology -- including nervous system tumors, neuroanatomy, neurodegenerative disease, muscle and nerve disorders, ophthalmologic pathology, neuro trivia, neuropathology gossip, job listings and anything else that might be of interest to a blue-collar neuropathologist. Brian E. Moore, MD: Neuropathologist, Memorial Medical Center in Springfield, Illinois. Co-Chair, Southern Illinois University School of Medicine Department of Pathology.

Proper citation: neuropathology blog (RRID:SCR_006825) Copy   

•   Source: SciCrunch Registry

http://cocomac.g-node.org/main/index.php?

Online access (html or xml) to structural connectivity ("wiring") data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies. The main database, contains data from tracing studies on anatomical connectivity in the macaque cerebral cortex. Also available are a variety of tools including a graphical simulation workbench, map displays and the CoCoMac-Paxinos-3D viewer. Submissions are welcome. To overcome the problem of divergent brain maps ORT (Objective Relational Transformation) was developed, an algorithmic method to convert data in a coordinate- independent way based on logical relations between areas in different brain maps. CoCoMac data is used to analyze the organization of the cerebral cortex, and to establish its structure- function relationships. This includes multi-variate statistics and computer simulation of models that take into account the real anatomy of the primate cerebral cortex. This site * Provides full, scriptable open access to the data in CoCoMac (you must adhere to the citation policy) * Powers the graphical interface to CoCoMac provided by the Scalable Brain Atlas * Sports an extensive search/browse wizard, which automatically constructs complex search queries and lets you further explore the database from the results page. * Allows you to get your hands dirty, by using the custom SQL query service. * Displays connectivity data in tabular form, through the axonal projections service. CoCoMac 2 was initiated at the Donders Institute for Brain, Cognition and Behaviour, and is currently supported by the German neuroinformatics node and the Computational and Systems Neuroscience group at the Juelich research institute.

Proper citation: CoCoMac (RRID:SCR_007277) Copy   

•   Source: SciCrunch Registry

http://www.oasis-brains.org/

Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.

Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy   

•   Source: SciCrunch Registry

http://senselab.med.yale.edu/odormapdb

OdorMapDB is designed to be a database to support the experimental analysis of the molecular and functional organization of the olfactory bulb and its basis for the perception of smell. It is primarily concerned with archiving, searching and analyzing maps of the olfactory bulb generated by different methods. The first aim is to facilitate comparison of activity patterns elicited by odor stimulation in the glomerular layer obtained by different methods in different species. It is further aimed at facilitating comparison of these maps with molecular maps of the projections of olfactory receptor neuron subsets to different glomeruli, especially for gene targeted animals and for antibody staining. The main maps archived here are based on original studies using 2-deoxyglucose and on current studies using high resolution fMRI in mouse and rat. Links are also provided to sites containing maps by other laboratories. OdorMapDB thus serves as a nodal point in a multilaboratory effort to construct consensus maps integrating data from different methodological approaches. OdorMapDB is integrated with two other databases in SenseLab: ORDB, a database of olfactory receptor genes and proteins, and OdorDB, a database of odor molecules that serve as ligands for the olfactory receptor proteins. The combined use of the three integrated databases allows the user to identify odor ligands that activate olfactory receptors that project to specific glomeruli that are involved in generating the odor activity maps.

Proper citation: Olfactory Bulb Odor Map DataBase (OdorMapDB) (RRID:SCR_007287) Copy   

•   Source: SciCrunch Registry

http://www.opensourcebrain.org

A resource for sharing and collaboratively developing computational models of neural systems. While models can be submitted and developed in any format, the use of open standards such as NeuroML and PyNN is encouraged, to ensure transparency, modularity, accessibility and cross simulator portability. OSB will provide advanced facilities to analyze, visualize and transform models in these formats, and to connect researchers interested in models of specific neurons, brain regions and disease states. Research themes include: Basal ganglia modelling, Cerebellar Granule cell modelling, Cerebellar modelling, Hippocampal modelling, Neocortical modelling, Whole brain models. Additional themes are welcome.

Proper citation: Open Source Brain (RRID:SCR_001393) Copy   

•   Source: SciCrunch Registry

http://www.GeneWeaver.org

Freely accessible phenotype-centered database with integrated analysis and visualization tools. It combines diverse data sets from multiple species and experiment types, and allows data sharing across collaborative groups or to public users. It was conceived of as a tool for the integration of biological functions based on the molecular processes that subserved them. From these data, an empirically derived ontology may one day be inferred. Users have found the system valuable for a wide range of applications in the arena of functional genomic data integration.

Proper citation: Gene Weaver (RRID:SCR_003009) Copy   

•   Source: SciCrunch Registry

http://drugdesigndata.org

Project portal's database of protein-ligand data sets provided by pharmaceutical partners that provide atomic details of drug mechanisms that will be used to improve computer-aided drug-design methods and thus accelerate drug discovery. The project aims to help companies release the high-quality data they have generated, which has incredible value to researchers working to improve methods of computer-aided drug discovery. Everyone stands to benefit from the ability to develop new medications more quickly and inexpensively. What computational chemists globally are trying to do is to make faster, more accurate, more predictive programs to speed up the process. Part of their mission is to engage the community in these challenges to test newly developed predictive algorithms.

Proper citation: Drug Design Data Resource (RRID:SCR_000497) Copy   

•   Source: SciCrunch Registry

http://retractionwatch.wordpress.com/

Retraction Watch is a blog of retractions in the scientific literature. It is maintained by Adam Marcus and Ivan Oransky and has been operating since August 2010.

Proper citation: RetractionWatch.com (RRID:SCR_000654) Copy   

•   Source: SciCrunch Registry

https://bams1.org/connectomes/standard_rat.php, https://bams1.org/connectomes/custom_rat.php

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9,2022. Database of information about brain region circuitry, it collates data from the literature on tract tracing studies and provides tools for analysis and visualization of connectivity between brain regions., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BAMS Connectivity (RRID:SCR_000561) Copy   

•   Source: SciCrunch Registry

http://www.labspaces.net/view_blog.php?ID=15

Blog about technology, molecular biology, and editorial comments on the current state of science on the internet. Brian Krueger PhD, is the owner, creator and coder of LabSpaces by night and a Molecular biologist by day. His posts are presented as opinion and commentary and do not represent the views of LabSpaces Productions, LLC, his employer, or his educational institution.

Proper citation: H2SO4Hurts (RRID:SCR_000686) Copy   

•   Source: SciCrunch Registry

http://lifespandb.sageweb.org/

Database that collects published lifespan data across multiple species. The entire database is available for download in various formats including XML, YAML and CSV.

Proper citation: Lifespan Observations Database (RRID:SCR_001609) Copy   

•   Source: SciCrunch Registry

http://blogs.plos.org/

PLoS Blogs has been set up to bring a select group of independent science and medicine bloggers together with the editors and staff who run our blogs. Our independent network is made up of writers who love science and medicine, and scientists and physicians that love to write. Here, you'll find an equal mix of blogs from journalists and researchers tackling diverse issues in science and medicine. There are three very distinct types of blogs on the PLoS Blogs network: the official PLoS blog, the PLoS journal blogs (collectively known as The PLoS Blogs), and blogs from the independent network (a.k.a. The PLoS Blogosphere) # The official PLoS blog: This content is produced, edited, and/or maintained by PLoS staff. # The journal blogs: This content is produced, edited, and/or maintained by PLoS journal staff: The current journal blogs are Speaking of Medicine (PLoS Medicine's blog) and everyONE (PLoS ONE's blog). # Our independent network of bloggers (The PLoS Blogosphere): This content is produced, edited, and/or maintained by the authors. * All of the content in The PLoS Blogosphere came from the minds of the authors. PLoS does not screen, edit, or otherwise meddle with content on the these blogs in any way. Our bloggers and our users are held to exactly the same standards, and the community guidelines apply to everyone that uses our site. If a blogger has posted content that you believe violates our site abuse policy, please contact PLoS. * Bloggers monitor their own comment threads: All comments will be reviewed by the author of the blog where you leave your thoughts. Just follow our simple community guidelines and we'll all get along just fine.

Proper citation: PLoS Blogs (RRID:SCR_001371) Copy   

•   Source: SciCrunch Registry

http://ibvd.virtualbrain.org/

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

Proper citation: Internet Brain Volume Database (RRID:SCR_002060) Copy   

•   Source: SciCrunch Registry