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33,754 results for “Enhancers”
Experimental verification of field-enhanced molecular vibrational scattering at single infrared antennas -- Dataset
<p>This is the nano-FTIR data set as shown in Figs 3 and 5 of our publication "Experimental verification of field-enhanced molecular vibrational scattering at single infrared antennas" by D. Virmani et al. This data set was acquired with a NeaSNOM microscopy (attocube AG) and can be opened with Gwyddion (https://gwyddion.net/). Please see the files "notes.txt" within the zip file provided for an identification of the individual data sets.</p> <p> </p> <p>nano-FTIR data is provided as interferogram files ending in "Interferograms.txt.txt" within the individual folders containing the string "NF S".<br>Row: not used<br>Column: not used<br>Run: number of interferogram acquisition<br>Depth: pixel number within one interferogram<br>M: not used<br>O0A: amplitude in V of the 0th demodulation order<br>O0P: phase in radians of the 0th demodulation order<br>O1A: amplitude in V of the 1st demodulation order<br>O1P: phase in radians of the 1st demodulation order<br>O2A: amplitude in V of the 2nd demodulation order<br>O2P: phase in radians of the 2nd demodulation order<br>O3A: amplitude in V of the 3rd demodulation order<br>O3P: phase in radians of the 3rd demodulation order<br>O4A: amplitude in V of the 4th demodulation order<br>O4P: phase in radians of the 4th demodulation order<br>O5A: amplitude in V of the 5th demodulation order<br>O5P: phase in radians of the 5th demodulation order</p> <p> </p> <p>Additionally, s-SNOM images for finding the antennas prior to nano-FTIR spectroscopy can be found in the folders containing the string "WL"</p> <p> </p> <p>For help on how to read or interpret these data please refer to the corresponding authors of the paper.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study.
<p>This repository contains the representative structures of the 20 clusters obtained, which constitute the “MD-adapted structure ensemble”: i.e., sets of atomic coordinates that capture the flexibility and the pre-miR20b (<a href="https://zenodo.org/api/files/ee12021f-4398-465a-9ff6-ddb7be32765f/ensemble_MD_2n7x.pdb?versionId=310a80f6-aa64-445d-8641-45faf9f1ac03">ensemble_MD_2n7x.pdb</a>) and Rbfox/pre-miR20b (<a href="https://zenodo.org/api/files/ee12021f-4398-465a-9ff6-ddb7be32765f/ensemble_MD_2n82.pdb?versionId=82d7afcb-8a92-4daa-8150-789dbd7b2474">ensemble_MD_2n82.pdb</a>) conformers suggested by MD simulations while still retaining the highest possible level of agreement with the primary NMR data.</p>
DataSet: Framing side effects positively: a way to enhance analgesia?
<p>Side effects are frequent in pharmacological pain management, potentially preceding analgesia and limiting drug tolerability. Discussing side effects is part of informed consent, yet can favor nocebo effects. This study aimed to test whether a positive suggestion regarding side effects, which could act as reminders of the medication having been absorbed, might favor analgesia in a clinical interaction model.</p> <p> </p> <p>Sixty-six healthy males participated in a study “to validate pupillometry as an objective measure of analgesia”. Participants were unknowingly randomized double-blind to positive vs control information about side effects embedded in a video regarding the study drugs. Sequences of moderately painful heat stimuli applied before and after treatment with diclofenac and atropine served to evaluate analgesia. Atropine was deceptively presented as a co-analgesic, but used to induce side effects. Adverse events (AE) were collected with the General Assessment of Side Effects (GASE) questionnaire prior to the second induced pain sequence. Debriefing fully informed participants regarding the purpose of the study and showed them the two videos.</p> <p> </p> <p>The combination of medication led to significant analgesia, without a between-group difference. Positive information about side effects increased the attribution of AE to the treatment compared to the control information. The total GASE score was correlated with analgesia, i.e., the more AEs reported, the stronger the analgesia. Interestingly, there was a significant between-groups difference on this correlation: the GASE score and analgesia correlated only in the positive information group. This provides evidence for a selective link between AEs and pain relief in the group who received the suggestion that AEs could be taken as a sign “that help was on the way”. During debriefing, 65% of participants said they would prefer to receive the positive message in a clinical context. These results suggest feasibility and validity to investigate such a framing of side effects in a clinical context, for example in patients with chronic pain.</p>
FANTOM5 transcribed enhancers in mm10
<p><strong>Overview</strong></p> <p>Transcribed enhancers were identified and their expression was quantified across all human FANTOM5 libraries, following the re-aligned FANTOM5 CAGE data upon mm10 (GRCm38) (obtained from http://fantom.gsc.riken.jp/5/datafiles/reprocessed/mm10_v1/basic/), and decomposition-based peak identification (obtained from https://zenodo.org/record/545682#.WPuNy1Pyv2Q) by Kawaji, Hideya.</p> <p><strong>Description</strong></p> <p>Transcribed enhancers were called based on bidirectional balanced RNA signatures as per Andersson et al (2014). Enhancers were only identified distal to known exons (+/-100bp region from boundaries) and transcription start sites (+/-300bp), defined by GENCODE vM7 annotation. In total, 44,138 enhancers were identified across 1,068 libraries and the expression was quantified. For details regarding the identification of transcribed enhancers from CAGE data, please see Andersson et al (2014) and blog post.</p> <p>Due to varying noise levels across FANTOM5 libraries and the intrinsic low expression levels of transcribed enhancers, library-specific noise levels were estimated to define of robust set of enhancers in each sample. In summary, for each library, expression was quantified in randomly sampled genomic regions distal to assembly gaps, DNase hypersensitive sites (ENCODE), known exons and gene TSSs (GENCODE vM7) to create a genomic background expression distribution. For each library, we called an enhancer active (used) if its expression was above the 99.9th quantile of the library’s genomic background expression distribution. The robust set of enhancers consist of those significantly expressed in at least one library.</p> <p>While this approach ensures less permissive enhancer calling in noisy libraries, for some libraries the noise threshold is zero meaning that a single CAGE tag is sufficient for calling an enhancer active. Furthermore, the possibility of detecting enhancer transcription is affected by sequencing depth, so the number of active enhancers per library might not be biologically meaningful to compare when sequencing depths differ.</p> <p><strong>Data files</strong><br> Each predicted enhancer is described in BED12 format with two blocks denoting the merged regions of transcription initiation on the minus and plus strands. The thickStart and thickEnd columns denote the inferred mid position between blocks of transcription initiation events. Expression and usage matrices are tab delimited and the first row gives the FANTOM5 CNhs IDs and the first column the enhancer ID (same as column 4 in BED file). Usage matrices contain zeroes and ones (0:not used, 1:used).</p> <ul> <li>enhancers (BED12 format)</li> <li>enhancer expression matrix (tab delimited, first row: CNhs IDs, first column: enhancer ID (coordinate))</li> <li>binary enhancer usage matrix (0:not used, 1:used, tab delimited, first row: CNhs IDs, first column: enhancer ID (coordinate))</li> </ul>
Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study.-PART 8
<p>Simulations of the miR20b RNA with the Case vdW modification to amber force field and OPC water molecules.</p>
Multilayer test masses to enhance the collapse noise
<p>multilayer.nb - Main program</p>
H3K27ac read counts in hematopoietic enhancers
<p>This dataset represents an integrated collection of hematopoietic enhancers. We downloaded all H3K27ac ChIP-seq and DNase I data from BLUEPRINT and hematopoietic DNase I data from ROADMAP. All DNase I data were processed using the Kundaje lab DNase pipeline version 0.3.0 <a href="https://github.com/kundajelab/atac_dnase_pipelines">https://github.com/kundajelab/atac_dnase_pipelines</a>]. The ChIP-seq samples were processed using the Kundaje lab AQUAS TF and histone ChIP-seq pipeline <a href="https://github.com/kundajelab/chipseq_pipeline">https://github.com/kundajelab/chipseq_pipeline</a>.</p> <p>For all experiments from BLUEPRINT we used the aligned reads provided by EBI. All ROADMAP samples were aligned using bowtie2 to the hg38 genome. DNase I peaks were called using MACS2 according to the default settings of the DNase pipeline. We merged all DNase I peak files and centered each merged peak on the summit of the strongest individual peak. H3K27ac reads were counted in a region of 2kb centered at the summit.</p> <p>For more details, see the GimmeMotifs manuscript: <a href="https://github.com/simonvh/gimmemotifs-manuscript/">https://github.com/simonvh/gimmemotifs-manuscript/.</a></p>
Data for manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing"
<p><strong>Data repository for manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing"</strong></p> <p>Here we describe the data stored in this archive, which has been used for the manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing", submitted to Geophysical Research Letters. This data archive includes two primary folders, one containing the CESM data and one containing the 2D model data. Within each of those folders are appropriately titled subfolders for the different model simulations and variables, in accordance with the descriptions provided in the manuscript. The following provides a description of the data within each folder: </p> <p><br> ----------------------------------------<br> ------------- CESM DATA --------------<br> ----------------------------------------<br> The CESM data folder has been split into four subfolders, one for each simulation: Control simulation (CESM_controlrun), 0.1 Sv freshwater flux simulation (CESM_0pt1Sv_FWFrun), the longer repeat of the 0.1 Sv freshwater flux simulation (CESM_0pt1Sv_FWFrun_repeat), and the 0.15 Sv freshwater flux simulation (CESM_0pt15Sv_FWFrun). Within each of these can be found folders for each of the 6 variables we have used: 4D temperature fields (TEMP), 4D salinity fields (SALT), 4D potential density fields (PD), 3D MOC fields (MOC), 3D mixed layer depth fields (HMXL), and 2D meridional heat transport fields (N_HEAT). Each CESM data file (one per monthly time step) is saved in netcdf format (.nc4), containing all appropriate dimensional data and descriptive meta data. Note that only the data used in the manuscript has been stored (e.g. only MOC data is provided for the CESM_0pt15Sv_FWFrun, while all fields have been provided for the CESM_0pt1Sv_FWFrun). </p> <p>----------------------------------------<br> ------------ 2D MODEL DATA -----------<br> ----------------------------------------<br> As described in the manuscript, the 2D model has been run using various choices of input parameter and freshwater flux. For each selection of parameter choice a new control run is first required, from which a set of experiments is then initiated. The 2D model data folder therefore contains 2 main subfolders, one containing the control run data for each of set of parameter choices (control_runs),and one containing the freshwater perturbation experiments for each set of parameter choices (fwp_change_exps). Within those, each control and experiment folder is titled according to the choices of vertical viscosity (kvd), Southern Ocean wind stress perturbation (txp), and the freshwater perturbation (fwp); when absent from the title, the default value for that variable is used (i.e. kvd=1e4 m2/s; txp=0.2 N/m2). The naming convention for the Southern Ocean wind stress is minpt07 for 0.13 N/m2 (i.e. minus 0.07) and pt07 for 0.27 N/m2. </p> <p>Model output data has been provided in ascii format for: the 2D Eulerian AMOC fields (MOC_EUL.dat.dat), 2D Quasi-Lagrangian fields (MOC_QLag.dat), 2D Salinity fields (salt2D.dat), 2D temperature fields (temp2d.dat), 2D density fields (rho.dat), the latitude values (ytdeg.dat; ydeg.dat) and depth values (zt.dat, zw.dat), and the timeseries values for a number of variables (timeseries.dat): The timeseries.dat data has 19 columns, of which the first 13 are useful: </p> <p> column: description:<br> 1 time (year)<br> 2 mean basin temperature (deg.C)<br> 3 mean basin salinity (psu)<br> 4 mean basin density (kg/m3) <br> 5 kinetic energy (KE) density<br> 6 potential energy (PE)<br> 7 mean surface heat flux<br> 8 mean surface salinity flux<br> 9 minimum meridional overturning streamfunction (Sv)<br> 10 maximum meridional overturning streamfunction (Sv)<br> 11 minimum advective poleward heat transport (PW)<br> 12 maximum advective poleward heat transport (PW)<br> 13 MOC (Sv) = max(psi) in the north Atlantic</p> <p>The final subfolder in the 2D model directory is titled 'additional_data': The first folder contains the snapshot data (fwp025_snapshots; one subfolder for each snapshot) for the experiment run with 25 cm/s freshwater flux as shown in Fig. S6, in which the data is stored in the same format as described above. The second folder contains the tendency terms as shown in Fig. S7 (fwp025_tendency; one subfolder per tendency term for each of the control and 25 cm/s freshwater flux experiment), in which the data is provided in ascii format for the 2D tendency term and the depth and latitude values. </p>
Immobilized Different Shape Gold Nanostructures on Printing Paper for Lable-Free Surface-enhanced Raman Spectroscopy
<p>Surface Enhanced Raman Spectroscopy (SERS) is spectroscopic technique of a label-free with high selectivity, and sensitivity.</p> <p><strong>Methods and Materials:</strong> Paper-based SERS active substrates with different grade gram per square meter (GSM) were prepared by adsorbing 40 nm spherical GNP and 40nm star-shaped gold nanostructures GNS. Besides the SERS evaluation and optimization, morphological parameters were found to strongly affect the substrates. The developed substrate was tested by the Raman reporter dye Methylene Blue (MB).</p> <p><strong>Results:</strong> These paper-based SERS active substrates are simple to prepare, easy to handle and cheap solid SERS substrates.</p> <p><strong>Conclusion: </strong>GNS with 135 GSM printing paper can easily use as highly active SERS active substrates, with enhancement factor EF= 10<sup>5</sup>.</p>
A possible formation channel for blue hook stars in globular cluster - II. Effects of metallicity, mass ratio, tidal enhancement efficiency and helium abundance
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2016MNRAS.463.3449L">Lei et al. (2016)</a>. MESA version 7211.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stw2242">10.1093/mnras/stw2242</a></p>
Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models
<p><strong>Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models”</strong><br> by V. Pandey, N. Hadadi and V. Hatzimanikatis</p> <p>"REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper: <br> • Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al.<br> • Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al.<br> • Metabolomics: This contains metabolomics data of aforementioned both studies.<br> • ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models. Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M). Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes.<br> • AlternativeMCS: We generated alternative states for MCS and saved results.<br> • FVAMM: This is the result flux variability analysis can be found in this folder.<br> • Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.</p> <p> </p>
A project-based learning approach for enhancing learning skills and motivation in software engineering
<p>Supplementary material for: <strong>A project-based learning approach for enhancing learning skills and motivation in software engineering</strong></p> <ul> <li><strong>DTSIPBLData.txt</strong>- Data set</li> <li><strong>ScriptRPBL.R:</strong> R Script tp perform the analysis of data.</li> <li><strong>Supplementary material- R Script.docx</strong>: Documented R Script</li> </ul>
Quercetin enhances the anti-tumor effects of BET inhibitors Supplementary Figures
<p>Supplementary Figures to "Quercetin enhances the anti-tumor effects of BET inhibitors" manuscript</p>
Serum response factor utilizes distinct promoter- and enhancer-based mechanisms to regulate cytoskeletal gene expression in macrophages.
<p>Cells of the monocyte/macrophage lineage play essential roles in tissue homeostasis and immune responses, but mechanisms underlying the coordinated expression of cytoskeletal genes required for specialized functions of these cells, such as directed migration and phagocytosis, remain unknown. Here, using genetic and genomic approaches, we provide evidence that serum response factor (SRF) regulates both general and cell type-restricted components of the cytoskeletal gene expression program in macrophages. Genome-wide location analysis of SRF in macrophages demonstrates enrichment of SRF binding at ubiquitously expressed target gene promoters, as expected, but also reveals that the majority of SRF binding sites associated with cell type-restricted target genes are at distal inter- and intragenic locations. Most of these distal SRF binding sites are established by the prior binding of the macrophage- and the B cell-specific transcription factor PU.1 and exhibit histone modifications characteristic of enhancers. Consistent with this, representative cytoskeletal target genes associated with these elements require both SRF and PU.1 for full expression. These findings suggest that SRF uses two distinct molecular strategies to regulate programs of cytoskeletal gene expression: a promoter-based strategy for ubiquitously expressed target genes and an enhancer-based strategy at target genes that exhibit cell type-restricted patterns of expression.</p>
Enhancing the Durability and the Service Life of Asphalt Pavements through Innovative Light-Induced Self-Healing Materials
<p>Corresponding data set for Tran-SET Project No. 17BLSU02. Abstract of the final report is stated below for reference:</p> <p>"The objective of this study was to evaluate the efficiency of a new generation of Ultraviolet (UV) light-induced self-healing polymers in enhancing the durability and self-healing properties of asphalt mixtures. Self-healing polymers were successfully synthesized in the laboratory and were characterized using Fourier Transform Infrared Spectroscopy (FTIR). In addition, Thermogravimetric Analysis (TGA) results showed that the synthesized polymers achieved the required thermal stability to resist asphalt mixture production processes. Viscosity results showed that addition of 5% Recycled Asphalt Shingle (RAS) and/or 20% Reclaimed Asphalt Pavement (RAP) caused an increase in the viscosity of the binder blends. However, a reduction in viscosity of the binder blends containing recycled asphalt materials was observed when adding self-healing polymers. Semi-Circular Bending (SCB) test results showed that addition of recycled asphalt materials negatively affected the cracking performance of mixtures. However, incorporation of self-healing polymer (SHP) and 48h of UV light exposure improved the cracking resistance. This behavior was more evident with mixtures prepared with an unmodified binder. Loaded-Wheel Test (LWT) results showed that the addition of the self-healing polymer led to an increase in the rut depth of the samples prepared with an unmodified binder. However, the final rut depth was less than 6 mm, which is an acceptable rutting performance. Thermal-Stress Restrained Specimen Test (TSRST) results showed that addition of 5% RAS negatively affected the low-temperature cracking performance of the mix. In contrast, 5% SHP enhanced the low-temperature cracking performance of the mix by increasing the fracture load and decreasing the fracture temperature. For mixtures prepared with an unmodified binder, the optimum crack healing efficiency was observed for the mixtures containing 5% RAS and 5% self-healing polymer and exposed to 48h of UV light. Yet, self-healing polymer did not perform well in mixtures prepared with PG 70-22M polymer-modified binder. This may be due to the interaction between the polymer in the binder and the self-healing polymer."</p>
Enhanced Synthetic Texas 2,000 bus (ACTIVSg2000) test case w/synthesized dynamic parameters of wind generation and protective devices
<p>Enhanced Synthetic Texas 2,000 bus (ACTIVSg2000) test case w/synthesized dynamic parameters of wind generation and protective devices<br> Compatible with PowerWorld v21, adapted from ACTIVSg2000.pwb (<a href="https://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg2000/">https://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg2000/</a>)<br> <br> Used as a test case for the paper: `Electrical Grid Resilience Metric with Uncertainty.' Power Systems Computation Conference, under review. </p> <p>Wind Dynamic Parameters:<br> Machine models:<br> -REGC_A: Renewable Energy Generator/Converter.</p> <p> Machine governors:<br> -WTGT_A: Wind Turbine Generator Drive-train Model.</p> <p> Machine excitation system:<br> -REEC_A: Renewable Energy Electrical Control Model.</p> <p>Protective relays:<br> -DLSH: Rate of Frequency Load Shedding Model (used on all loads in network).<br> -LVSH: Undervoltage Load Shedding Model (used on all loads in network).<br> -TIOCR1: Time inverse overcurrent relay (used on all lines in network).<br> -FRQTPAT: Under frequency generator trip relay (used on all machines).<br> -LHVRT: Low/High Voltage Ride through Generator Protection (used on all machines).</p> <p> </p> <p>This research was completed by Idaho National Laboratory with funding<br> from the U.S. Department of Energy Wind Energy Technologies Office. Idaho National Laboratory is operated<br> by Battelle Energy Alliance under contract No. DE AC07-05ID14517.</p>
Augmented Reality Enhancing the Inspections of Transportation Infrastructure: Research, Education, and Industry Implementation
<p>Corresponding data set for Tran-SET Project No. 18STUNM03. Abstract of the final report is stated below for reference:</p> <p>"Transportation infrastructure needs continuous monitoring that is conducted by field inspectors regularly in the field. Currently, infrastructure inspectors climb, measure, and photograph structures annually to inform repair needs and prioritize decisions. In order to promote and accelerate early learner's expertise in decision-making capabilities during infrastructure inspections, this research project developed various software applications using augmented reality (AR) as an inspection tool for bridges and bridge management, more specifically. By objectively quantifying infrastructure field inspections, inspectors can make more accurate field assessments and managers can make better-informed decisions. This project collaborated with stakeholders, national laboratories, DOT agencies such as NCHRP and NMDOT, and local owners like the City of Albuquerque, to inform the needs of AR for field inspections. The results of this study summarized the current limitations of visual inspections from the perspective of the various owners, as well as pilot developments of AR applications and their benchmarked accuracy in comparison with visual methods. The education and training aspect of this project included teaching and exposing AR to high school students, community college students, undergraduate students, and graduate students, as well as industry (bridge inspectors). This research project’s outcome includes a webinar free to access in the NCHRP national website on this topic. The conclusion of this research is that AR can be an effective tool and that industry is interested in specific programming of AR software that matches their bridge management needs."</p>
Dataset of "Enhancement of impact heating in pressure-strengthened rocks in oblique impacts"
<p>This dataset contains input files for iSALE-3D and sample of a python script for the paper "Enhancement of impact heating in pressure-strengthened rocks in oblique impacts" by S. Wakita et al.<br> <br> Please note that usage of the iSALE-3D code is restricted to those who have contributed to the development of iSALE-2D, and iSALE-2D is distributed on a case-by-case basis to academic users in the impact community. It requires a registration from the iSALE webpage (http://www.isale-code.de) and usage of iSALE-2D and computational requirements are also shown in that page. Please also note that pySALEPlot in the current stable release of iSALE-2D (Dellen) would not work for the data from iSALE-3D.<br> </p>
Datas for Leveraging ecosystems responses to enhanced rock weathering in mitigation scenarios
<p>This dataset contains the jupyter notebook calibrating the P-cycle emulator based on the ORCHIDEE-CNP outputs produced by Daniel S. Goll. The zip file contains a set of results from the simulations. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.