Database of microarray analysis of twelve major classes of fluorescent labeled neurons within the adult mouse forebrain that provide the first comprehensive view of gene expression differences. The publicly available datasets demonstrate a profound molecular heterogeneity among neuronal subtypes, represented disproportionately by gene paralogs, and begin to reveal the genetic programs underlying the fundamental divisions between neuronal classes including that between glutamatergic and GABAergic neurons. Five of the 12 populations were chosen from cingulate cortex and included several subtypes of GABAergic interneurons and pyramidal neurons. The remaining seven were derived from the somatosensory cortex, hippocampus, amygdala and thalamus. Using these expression profiles, they were able to construct a taxonomic tree that reflected the expected major relationships between these populations, such as the distinction between cortical interneurons and projection neurons. The taxonomic tree indicated highly heterogeneous gene expression even within a single region. This dataset should be useful for the classification of unknown neuronal subtypes, the investigation of specifically expressed genes and the genetic manipulation of specific neuronal circuit elements. Datasets: * Full: Here you can query gene expression results for the neuronal populations * Strain: Here you can query the same expression results accessed under the full checkbox, with one additional population (CT6-CG2) included as a control for the effects of mouse strain. This population is identical to CT6-CG (YFPH) except the neurons were derived from wild-type mice of three distinct strains: G42, G30, and GIN. * Arlotta: Here you can query the same expression results accessed under the full checkbox, with nine additional populations from the dataset of Arlotta et al., 2005. These populations were purified by FACS after retrograde labeling with fluorescent microspheres. Populations are designated by the prefix ACS for corticospinal neurons, ACC for corticocallosal neurons and ACT for corticotectal neurons, followed by the suffix E18 for gestational age 18 embryos, or P3, P6 and P14 for postnatal day 3, 6 and 14 pups. For each successful gene query the following information is returned: # Signal level line plot: Signal level is plotted on Y-axis (log base 2) for each sample. Samples include the thirty six representing the twelve populations profiled in Sugino et al. In addition, six samples from homogenized (=dissociated and but not sorted) cortex are included representing two different strains: G42-HO is homogenate from strain G42, GIN-HO is homogenate from stain GIN. # Signal level raster plots: Signal level is represented by color (dark red is low, bright red is high) for all samples. Color scale is set to match minimum (dark red) and maximum (bright yellow) signal levels within the displayed set of probe sets. # Scaled signal level raster plots: Same as 2) except color scale is adjusted separately for each gene according to its maximum and minimum signal level. # Table: Basic information about the returned probe sets: * Affymetrix affyid of probe set * NCBI gene symbol, NCBI gene name * NCBI geneID * P-value score from ANOVA for each gene is also given if available (_anv column). P-value represents the probability that there is no difference in the expression across cell types.

Supplementary material A.3 for the paper 'IDPredictor: predict database links in biomedical database'. Abstract: Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data are spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge excerpt out of the interlinked databases. A prerequisit for supporting the concept of an integrated data view is the to acquiring insights into cross-references among database entities. But only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predict and extracts cross-references from multiple life science databases and thier possible referenced data targets. We study the retrieval quality of our method and the relationship between manually crafted relevance ranking and relevance ranking based on cross-references, and report on first, promising results.

Southern California PmP phase data expanded by a deep learning technique PmPNet.

Here, we employ the GengNet [26], and the sliding window length is fixed at 200ms for all experiments.

This page describes the contents of a database of 1.7 million model neurons. This database is available for interested researchers after contacting the creators, but is not web accessible. The construction and analysis of the database are described in detail in Prinz AA, Billimoria CP, Marder E (2003). Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90: 3998-4015. Because of its size (over 6 GB even in the zipped version), it is not practicable to download the database over the internet. Instead, we have made multiple copies of the database on sets of two DVDs each. We are happy to send a set of DVDs to anybody who is interested upon e-mail request to Astrid Prinz.

In this repository, we included code to prepare dataset, train gemnet model, build the faiss index, search the faiss index and visualize the searched results in the notebook faiss-gemnet-qm9-mp.ipynb. It reproduced our examples in the manuscript for the QM9 and the Materials Project dataset. For the OC20 dataset, we did not include its related data here because of its large size (> 50 GB), the code to process the OC20 dataset is almost the same as the code included in the notebook for the QM9 dataset.

We include the intermediate data (GemNet checkpoints, lmdb, faiss index and the searched result for the QM9 and the Materials project in the directory example-data. We also put the GemNet checkpoint for the OC20 dataset in this directory. The training and evaluation of the Gaussian regression process model using the searched molecules for the query Benzene are demonstrated in the ben-gp-data directory, in which the qm9-gp-gemnet-morgan-random-nrg.ipynb can be run on Colab.

Data for Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface Jan Weinreich, Anton Römer, Martín Leandro Paleico, and Jörg Behler

53 841 reference structures of alpha brass (less 40 % Zn) with following split - 4009 brass clusters - 8492 molten brass bulk structures - 8964 copper slabs, and 16 878 brass slabs - 5377 copper bulk structures - 10 121 brass bulk structures have been included.

53 841 total energies and 8 903 340 force components. The ranges of values for the energies and force components to be fitted have a width of about 2 eV/atom and 15 eV/Å, respectively. However, some structures may have slightly higher Zn content as discussed in Fig 3 (https://arxiv.org/abs/2001.10906)

The archive contains an easily usable npz file as well as the original input.data file used to fit the potential energy surface. Additionally a Jupyter notebook describes in great detail how the data was converted to the npz format and how to read the data e.g. for subsequent use with python. In addition an example VASP calculation was added to provide detailed information about how the reference data was calculated with DFT.

DETAILS: - DFT PB VASP-5.3 target accuracy of the total energy few meV/atom - Convergence tests with respect to the number of k-points showed that in order to fulfill this criterion a k-point grid of 12 × 12 × 12 is needed for a conventional four-atom copper fcc unit cell with a lattice constant of about 3.63 Å along with a plane wave cutoff energy of 500 eV and projector augmented wave potentials.(61,63) - Larger systems have been calculated using an adapted k-point grid corresponding to the same k-point density. The Γ-point centered k-point grids have been constructed employing the Monkhorst–Pack scheme. For surface calculations, 4–14 layer slabs with a total vacuum thickness of at least 8 Å have been used. - In the case of cluster calculations, which have also been treated in a periodic setup, the periodic images of the clusters have been separated by at least 8 Å in all three spatial directions. The convergence of very large clusters with diameters of d ≈ 22 Å, which have been used to include specific atomic environments in the data set, has been extensively tested and we found that using the Γ-point only is sufficient to reach the required convergence level.

A database to support research on drugs for the treatment of different neurological disorders. It contains agents that act on neuronal receptors and signal transduction pathways in the normal brain and in nervous disorders. It enables searches for drug actions at the level of key molecular constituents, cell compartments and individual cells, with links to models of these actions.

Version 1.0 database for neuro-endocrine-immune (dbNEI) is a web-based knowledge resource specific for the NEI systems. It provides a knowledge environment for understanding the main regulatory systems of NEI in a molecular level. dbNEI provides a knowledge environment for understanding the main regulatory systems of NEI in a molecular level. dbNEI collects 1,058 NEI related signal molecules, their 940 interactions and 72 affiliated tissues from the Cell Signaling Networks database and manually selects 982 NEI papers from PubMed. NEI related information, such as signal transductions, regulations and control subunits, are integrated. Especially, dbNEI represents as graphic visualization, by which control subunits can be automatically obtained according to the inquiring issues. Version 2.0: We updated the database in four aspects. 1. Recruiting new NEI genes and compounds. 2. Adding KEGG,HPRD,Transcription factor and microRNA target relations. 3. Collecting drug-gene and disease-gene relation. 4. Building multi-layer network for drug-NEI-disease.

This database contains the reference data used for direct force training of Artificial Neural Network (ANN) interatomic potentials using the atomic energy network (ænet) and ænet-PyTorch packages (https://github.com/atomisticnet/aenet-PyTorch). It also includes the GPR-augmented data used for indirect force training via Gaussian Process Regression (GPR) surrogate models using the ænet-GPR package (https://github.com/atomisticnet/aenet-gpr). Each data file contains atomic structures, energies, and atomic forces in XCrySDen Structure Format (XSF). The dataset includes all reference training/test data and corresponding GPR-augmented data used in the four benchmark examples presented in the reference paper, “Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation”. A hierarchy of the dataset is described in the README.txt file, and an overview of the dataset is also summarized in supplementary Table S1 of the reference paper.

This database contains the reference data used for direct force training of Artificial Neural Network (ANN) interatomic potentials using the atomic energy network (ænet) and ænet-PyTorch packages (https://github.com/atomisticnet/aenet-PyTorch). It also includes the GPR-augmented data used for indirect force training via Gaussian Process Regression (GPR) surrogate models using the ænet-GPR package (https://github.com/atomisticnet/aenet-gpr). Each data file contains atomic structures, energies, and atomic forces in XCrySDen Structure Format (XSF). The dataset includes all reference training/test data and corresponding GPR-augmented data used in the four benchmark examples presented in the reference paper, "Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation". A hierarchy of the dataset is described in the README.txt file, and an overview of the dataset is also summarized in supplementary Table S1 of the reference paper.

Database of interactive neural computation computer models at levels ranging from simple linear filters to large-scale networks of spiking units. Interface tools are provided while browsing and exploring models.

Same as cropped images here, just converted to PNG instead http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

I do not own this data. All credits go to:

"From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001, "Acquiring Linear Subspaces for Face Recognition under Variable Lighting", PAMI, May, 2005 "the Extended Yale Face Database B"

The cropped dataset only contains the single P00 pose.

Data format is like yaleBxx_P00A(+/-)aaaE(+/-)ee

• xx = Subject ID
• (+/-)aaa = Azimuth angle
• (+/-)ee = Elevation angle

For example the file yaleB38_P00A+035E+65.png is of subject 38, in pose 00, with light source at (+035, +65) degrees (azimuth, elevation) w.r.t the camera.

We use the same sliding window length (200ms) for all experiments mentioned bellow.

This repository contains all processed, scripts and statistical analysis to generate the results of the manuscript entitled "The neural representation of an auditory spatial cue in primate cortex".Data structureThe raw data is compressed in multiple zip files within the raw_data_zip folder.The scripts_and_data directory contains sub-directories with the scripts, data, and figures pertinent to the modality defined by the directory name.All the statistical analyses can be found in the 'statistical_analyses' folder, in which the analysis for each specific modality are given by the folder name.All .sqlite databases are structured hierarchically by the tables 'subjects', 'measurement_info', and 'stimuli'. All other tables will share the same level of hierarchy, in which the specific rows are linked to each subject, measurement, and stimuli by the columns 'id_subject', 'id_measurement', and 'id_stimuli'.All R, python, and Matlab scripts will access the different databases (or data files) directly to generate the different figures and analyses.Details on how to run the code can be found in the following linkhttps://gitlab.com/jundurraga/meg_eeg_behavioural

INTRODUCTION: Psy.Card Database contains the neuronal proteins responsible for ICD-11 psychiatric conditions. METHODOLOGY: The database reviews the neuronal proteome (Martins et al., 2023) obtained from neuroproteomics up to 2023. It follows the same strategy already published by the author for the acquisition of the Neuro.Card database (Martins, 2021), which identified 2416 neuronal proteins responsible for ICD-11 neuropsychiatric disorders. RESULTS: The Psy.Card database identified 335 proteins (Martins et al., 2023) involved in neuronal processes and covers data on proteins with altered expression profiles. The results of this neurobiological approach imply that alterations in neurotransmission may be partially responsible for mental pathophysiology.

In vitro electrophysiology of iPSC-derived cortical neural networks (healthy vs. induced proteinopathy) using customised microfluidic chips interfaced with 60-electrode microelectrode arrays. The recordings were obtained using the M2100 Multichannel Systems platform.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 19, 2012. Due to budgetary constraints, the National Center for Biotechnology Information (NCBI) has discontinued support for the NCBI GENSAT database, and it has been removed from the Entrez System. The Gene Expression Nervous System Atlas (GENSAT) project involves the large-scale creation of transgenic mouse lines expressing green fluorescent protein (GFP) reporter or Cre recombinase under control of the BAC promoter in specific neural and glial cell populations. BAC expression data for all the lines generated (over 1300 lines) are available in online, searchable databases (www.gensat.org and the Database of GENSAT BAC-Cre driver lines). If you have any specific questions, please feel free to contact us at info_at_ncbi.nlm.nih.gov The GENSAT project aims to map the expression of genes in the central nervous system of the mouse, using both in situ hybridization and transgenic mouse techniques. Search criteria include gene names, gene symbols, gene aliases and synonyms, mouse ages, and imaging protocols. Mouse ages are restricted to E10.5 (embryonic day 10.5), E15.5 (embryonic day 15.5), P7 (postnatal day 7), and Adult (adult). The project focuses on two techniques * Evaluation of unmodified mice lines for expression of a given gene using radiolabelled riboprobes and in-situ hybridization. * Creation of transgenic mice lines containing a BAC construct that expresses a marker gene in the same environment as the native gene

Curated database of published models so that they can be openly accessed, downloaded, and tested to support computational neuroscience. Provides accessible location for storing and efficiently retrieving computational neuroscience models.Coupled with NeuronDB. Models can be coded in any language for any environment. Model code can be viewed before downloading and browsers can be set to auto-launch the models. The model source code has to be available from publicly accessible online repository or WWW site. Original source code is used to generate simulation results from which authors derived their published insights and conclusions.

A database of quantum mechanical calculations on organic photovoltaic candidate molecules. Related Publications: Peter C. St. John, Caleb Phillips, Travis W. Kemper, A. Nolan Wilson, Michael F. Crowley, Mark R. Nimlos, Ross E. Larsen. (2018) Message-passing neural networks for high-throughput polymer screening arXiv:1807.10363