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https://data.gov.au/

Portal as central source of Australian open government data. Used to find, explore and reuse Australia public data. Anyone can access the anonymised public data published by federal, state and local government agencies, publicly funded research data and datasets from private institutions that are in public interest.

Proper citation: Australian Government Data Portal (RRID:SCR_019159) Copy   

•   Source: SciCrunch Registry

https://hdmf.readthedocs.io/

Open source software Python package for working with hierarchical data. Provides APIs for specifying data models, reading and writing data to different storage backends, and representing data with Python object.Used for working with standardizing, reading, and writing hierarchical object data.

Proper citation: Hierarchical Data Modeling Framework (RRID:SCR_021303) Copy   

•   Source: SciCrunch Registry

https://github.com/ttrogers/DecodingDynamic

Data, code, and notebooks for replicating analyses reported in Rogers et al., Evidence for deep, distributed and dynamic semantic code in human ventral anterior temporal cortex.

Proper citation: DecodingDynamic (RRID:SCR_021099) Copy   

•   Source: SciCrunch Registry

https://github.com/EpistasisLab/ReBATE

Open source software Python package to compare relief based feature selection algorithms used in data mining. Used for feature selection in any bioinformatics problem with potentially predictive features and target outcome variable, to detect feature interactions without examination of all feature combinations, to detect features involved in heterogeneous patterns of association such as genetic heterogeneity .

Proper citation: ReBATE (RRID:SCR_017139) Copy   

•   Source: SciCrunch Registry

https://edspace.american.edu/openbehavior/

Repository of open source tools for behavioral neuroscience research. OpenBehavior features hardware (tools, devices, apparatuses), as well as software for data acquisition and analysis and for the investigation of animal behavior and cognition. Dedicated to accelerating research through promotion of collaboration and open source projects.

Proper citation: OpenBehavior (RRID:SCR_015938) Copy   

•   Source: SciCrunch Registry

https://www.thermofisher.com/order/catalog/product/CHROMELEON7

Software system to support chromatography operations, to ensure data quality and manage all the analytical processes from instrument control, to raw data storage and processing, through to generating the final results.

Proper citation: Chromeleon Chromatography Data System (CDS) Software (RRID:SCR_016874) Copy   

•   Source: SciCrunch Registry

https://pynwb.readthedocs.io/en/latest/

Software Python package for working with Neurodata stored in Neurodata Without Borders files. Software providing API allowing users to read and create NWB formatted HDF5 files. Developed in support to NWB project with aim of spreading standardized data format for cellular based neurophysiology information.

Proper citation: PyNWB (RRID:SCR_017452) Copy   

•   Source: SciCrunch Registry

http://download.ant-neuro.com/matlab/

Software as an EEGLAB tool used for LIBEEP recordings.

Proper citation: Libeep EEGLAB plugin (RRID:SCR_016334) Copy   

•   Source: SciCrunch Registry

https://github.com/BioDepot/nbdocker

Software tool as Jupyter Notebook extension for Docker. Each Docker container encapsulates its individual computing environment to allow different programming languages and computing environments to be included in one single notebook, provides user to document code as well as computing environment.

Proper citation: nbdocker (RRID:SCR_017159) Copy   

•   Source: SciCrunch Registry

http://locustdb.genomics.org.cn/

The migratory locust (Locusta migratoria) is an orthopteran pest and a representative member of hemimetabolous insects. Its transcriptomic data provide invaluable information for molecular entomology study of the insect and pave a way for comparative studies of other medically, agronomically, and ecologically relevant insects. This first transcriptomic database of the locust (LocustDB) has been developed, building necessary infrastructures to integrate, organize, and retrieve data that are either currently available or to be acquired in the future. It currently hosts 45,474 high quality EST sequences from the locust, which were assembled into 12,161 unigenes. This database contains original sequence data, including homologous/orthologous sequences, functional annotations, pathway analysis, and codon usage, based on conserved orthologous groups (COG), gene ontology (GO), protein domain (InterPro), and functional pathways (KEGG). It also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. LocustDB also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. It starts with the first transcriptome information for an orthopteran and hemimetabolous insect and will be extended to provide a framework for incorporation of in-coming genomic data of relevant insect groups and a workbench for cross-species comparative studies.

Proper citation: Migratory Locust EST Database (RRID:SCR_008201) Copy   

•   Source: SciCrunch Registry

https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 29,2025. Electroencephalogram (EEG) data recorded from invasive and scalp electrodes. The EEG database contains invasive EEG recordings of 21 patients suffering from medically intractable focal epilepsy. The data were recorded during an invasive pre-surgical epilepsy monitoring at the Epilepsy Center of the University Hospital of Freiburg, Germany. In eleven patients, the epileptic focus was located in neocortical brain structures, in eight patients in the hippocampus, and in two patients in both. In order to obtain a high signal-to-noise ratio, fewer artifacts, and to record directly from focal areas, intracranial grid-, strip-, and depth-electrodes were utilized. The EEG data were acquired using a Neurofile NT digital video EEG system with 128 channels, 256 Hz sampling rate, and a 16 bit analogue-to-digital converter. Notch or band pass filters have not been applied. For each of the patients, there are datasets called ictal and interictal, the former containing files with epileptic seizures and at least 50 min pre-ictal data. the latter containing approximately 24 hours of EEG-recordings without seizure activity. At least 24 h of continuous interictal recordings are available for 13 patients. For the remaining patients interictal invasive EEG data consisting of less than 24 h were joined together, to end up with at least 24 h per patient. An interdisciplinary project between: * Epilepsy Center, University Hospital Freiburg * Bernstein Center for Computational Neuroscience (BCCN), Freiburg * Freiburg Center for Data Analysis and Modeling (FDM).

Proper citation: Electroencephalogram Database: Prediction of Epileptic Seizures (RRID:SCR_008032) Copy   

•   Source: SciCrunch Registry

http://amazonia.montp.inserm.fr/

A web interface and associated tools for easy query of public human transcriptome data by keyword, through thematic pages with list annotations. Amazonia provides a thematic entry to public transcriptomes: users may for instance query a gene on a Stem Cells page, where they will see the expression of their favorite gene across selected microarray experiments related to stem cell biology. This selection of samples can be customized at will among the 6331 samples currently present in the database. Every transcriptome study results in the identification of lists of genes relevant to a given biological condition. In order to include this valuable information in any new query in the Amazonia database, they indicate for each gene in which lists it is included. This is a straightforward and efficient way to synthesize hundreds of microarray publications., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: AmaZonia: Explore the Jungle of Microarrays Results (RRID:SCR_008405) Copy   

•   Source: SciCrunch Registry

http://www.molecularbrain.org/

MolecularBrain is an attempt to collect, collates, analyze and present the microarray derived gene expression data from various brain regions side by side. Transcription Profile of any gene in Mouse (online) and Human Brain (not yet) can be accessed as a histogram along with links to access various aspects of that gene. The expression levels were calculated from microarray data deposited at GEO (Gene expression omnibus). The molecular brain database could be searched using the built in search tool with the terms Entrez GeneID, gene symbol, synonym or description. Gene information along with their expression values can be also accessed from the alphabetical list of gene symbols on the footer. The protocol and GEO sample information is available.

Proper citation: Molecular Brain: Transcription Profiles of Mouse and Human Brains (RRID:SCR_008689) Copy   

•   Source: SciCrunch Registry

http://neuromorphometrics.com/?page_id=23

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

Proper citation: Manually Labeled MRI Brain Scan Database (RRID:SCR_009604) Copy   

•   Source: SciCrunch Registry

http://connectivity.brain-map.org/

Map of neural connections in mouse brain, built on an array of transgenic mice genetically engineered to target specific cell types. In addition to the connectivity data, information about the transgenic mouse lines and genetic tracers is available. Consists of high resolution 2-D projectivity image data that can be viewed side-by-side with the associated reference atlas and other reference datasets. Enables 3-D visualization and spatial/ontological search of connectivity models through a combination of manual and informatics analyses.

Proper citation: Allen Mouse Brain Connectivity Atlas (RRID:SCR_008848) Copy   

•   Source: SciCrunch Registry

https://github.com/pachterlab/kb_python

Software Python package that wraps kallisto and bustools single-cell RNA-seq workflow. Used for single-cell RNA-seq pre-processing. Simplifies downloading and running of kallisto and bustools programs. Consists of kb ref and kb count commands. kb ref builds or downloads species specific index for pseudo alignment of reads and must be run prior to kb count and it runs kallisto index. kb count runs kallisto and bustools programs and is used for pre-processing of data from variety of single-cell RNA-seq technologies, and for number of different workflows (e.g. production of gene count matrices, RNA velocity analyses, etc.).

Proper citation: kb_python (RRID:SCR_018213) Copy   

•   Source: SciCrunch Registry

https://github.com/dorianps/LESYMAP

Software R package to conduct lesion-to-symptom mapping from human MRI data.Takes lesion maps and cognitive performance scores from patients with stroke, and maps brain areas responsible for cognitive deficit.

Proper citation: LESYMAP (RRID:SCR_017967) Copy   

•   Source: SciCrunch Registry

http://www.cbrc.jp/htbin/show_tffactor_mw

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 13, 2016.

A dataset about transcriptional regulation in eukaryotic cells, including data such as transcription factors and their binding sites and profiles. Resource is in Chinese.

Proper citation: TFFACTOR (RRID:SCR_000519) Copy   

•   Source: SciCrunch Registry

http://montana.eagle-i.net/i/0000012b-00be-4e65-df3b-3fdc80000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27, 2023. Core for Microarray analysis, Database development, Systems biology analysis, Genome assembly, Pathway data analysis, Expression data analysis, Metagenomics analysis. To maintain equipment and software for bioinformatic research, promote bioinformatics education on the MSU campus, and provide training and support to biologists implementing bioinformatics tools in their research.

Proper citation: Montana State University Bioinformatics Core Facility (RRID:SCR_009937) Copy   

•   Source: SciCrunch Registry

https://wistar.org/research-discoveries/shared-resources/bioinformatics-facility

Core provides High Throughput Data Analysis, Customized Bioinformatics Services, Custom Programming, High Performance Computing, Data management. Located in the Center for Systems and Computational Biology. Provides Cancer Center investigators with database management, software application support, expertise in statistical analyses and computational modeling of biomedical research data.

Proper citation: Wistar Bioinformatics Core Facility (RRID:SCR_010203) Copy   

•   Source: SciCrunch Registry