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4,694 results for “data analysis”
MADFORWATER: WP1: Water and water-related vulnerabilities in Egypt, Morocco and Tunisia: Task1.2: Analysis and mapping of water stress, water vulnerability and potential for water reuse in Egypt, Morocco and Tunisia: Subtask1.2.b: Data collection on water stress and vulnerability: Souss-Massa Region Subset
<p>This folder contains the dataset that I used to write my conference paper "Groundwater Resources Scarcity in Souss-Massa Region and Alternative Solutions for Sustainable Agricultural Development"</p>
Relevant model data supporting the analysis and conclusion of Goll et al., GRL, 2018
<p>Model simulations which form the basis of the publication: Goll et al.: Low phosphorus availability decreases susceptibility of tropical primary productivity to droughts, GRL, 2018 </p> <p>The data is in folder which labels are composed of <model revision><site identifier>Silt<model configuration>, where model configuration "C" is the simulation with optimal nutrient availability and "CNPA8" is the simulation with prognostic nutrient availability. The site identifier are "BRSa1", "BRSa3", "BRMa2" and are explained in the main manuscript. </p> <p> </p> <p> </p> <p> </p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE paper)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the journal "Concurrency and Computation: Practice and Experience", under the title <em>"Performance Modeling of a Geophysics Application to Accelerate the Tuning of Over-decomposition Parameters through Simulation"</em>.</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - Mouse feeding behavior dataset
<p>Dataset contains feeding and drinking behavioral recordings of C57BL6/J male mice. Mice were distributed into 2 groups (9 control mice and 8 high-fat diet mice) and tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a></p> <p>The data set consist in:</p> <p>- a "mouse_recordings" folder containing a CSV file containing mouse recordings and the files.</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "phases" folder containing a CSV file containing experimental phases.</p> <p>- a "chromHMM_files" folder containing a cellmarkfiletable table used by chromHMM to learn a HMM model</p>
Data for: A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication
<p>All the data, code, analyses, and figures used in the study entitled: "A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication" <em>(doi: https://doi.org/<a href="http://bb2sz3ek3z.search.serialssolutions.com/?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&__char_set=utf8&rft_id=info:doi/10.1101/247650&rfr_id=info:sid/libx&rft.genre=article">10.1101/247650</a>)</em></p> <p><strong>Abstract</strong></p> <p>Geneticists have long used olfactory conditioning techniques in <em>Drosophila</em> to identify the neurons and genes that mediate learning. While this method has characterized an abundance of memory-related genes, little is known about how these genes induce short-term memory (STM) via signaling pathways; characterizing these networks will be essential to developing mechanistic models of memory formation. Here, we investigated why elucidating the STM pathways has been relatively slow. One possibility is that the STM evidence base is weak due to publication of poorly reproducible results, as has been observed in other fields. We examined this hypothesis by performing a systematic review and subsequent meta-analysis of the STM genetics field. Using several metrics to quantify the variation between discovery articles and follow-up studies, we found that seven genes were highly replicated, showed no publication bias, and had generally high reproducibility. However, the remaining ~80% memory genes have not been replicated since their initial discovery. Although we observed only a few studies that investigated gene interactions, the reviewed genes could together account for >1000% memory. This large summed effect size indicates either that some of the gene findings are not reproducible, that many memory genes participate in shared pathways, or that current protocols lack the specificity needed to identify core plasticity memory genes. Mechanistic theories of memory and cognition will require the convergence of evidence from system, circuit, cellular, molecular, and genetic experiments. As this study demonstrates, systematic data synthesis is an essential tool for this integrated brain science.</p>
Manipulation of netCDF data with R for climate change research: Multi-model analysis for CMIP5 models.
<p>Geoscientists now live in a world with an exponential growth in digital data and methods.<br> Climate change studies usually describe computational methods informally. Climate scientists seek to<br> share their information, the justification of reproducible research has received increasing attention in<br> geosciences. To have it in an open-source format makes it easier to interchange not only with fellow<br> scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source<br> computer language powerful and highly extensible that can promotes reproductive science techniques in a<br> easier way. R is highly accessible for non-computational scientists when coupled with packages like<br> ‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out<br> complex data analysis. In this paper we have assessed the power of R language for manipulating climate<br> data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we<br> have proposed an example of best practices to handle model ensembles. This is the first study to our<br> knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster<br> package capabilities provides efficient access to the multi-model, with crucial applications in climate<br> change research. In recent years more than 100 peer-reviewed scientific publications have used the<br> CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools<br> to author papers and disseminate information about the process and products of their research.</p>
Partitioned Image Data for Machine Learning Analysis of Molecular Biology Figures
<p><strong> Corpus Composition</strong></p> <p>This data collection provides four types of hand-curated images from open access research articles images. The types are:</p> <ol> <li>chart (n=811): data displays such as bar charts, scatterplots, line graphs, etc.</li> <li>diagram (n=816): any general conceptual diagram</li> <li>gel (n=1182): the output of electrophoresis experiments in Northern, Western, or Southern Blot experiments. </li> <li>histology (n=3458): microscope images of tissue with histological staining</li> </ol> <p>The images are simply organized in subdirectories as individual files. File names are based on PubMed Id and Figure number. </p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - D. melanogaster behavior dataset obtained with JAABA
<p>Dataset contains <em>Drosophila melanogaster </em>behavioral annotations of chasing. The dataset is formed by 20 GAL4 line flies group from a TrpA activation screen with increased propensity to chasing and by 19 pBDPGAL4U line (control) flies group. Ctrax motor trajectories derived from the original video-recordings (1000 seconds) were downloaded from this <a href="https://sourceforge.net/projects/jaaba/files/Sample%20Data/sampledata_v0.1.zip/download">link</a> and used to obtain the chasing behavioral annotations using <a href="http://jaaba.sourceforge.net/">JAABA</a> <a href="https://www.nature.com/articles/nmeth.2281">10.1038/nmeth.2281</a>: </p> <p>The data set consist in:</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "perframe_TrpA" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the GAL4 line.</p> <p>- a "perframe_pBDPGAL4" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the control line.</p> <p>- a "scores" folder including the JAABA chasing annotations in two mat file one for each fly line.</p>
Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"
<p>Data related to the publication Murschhauser <em>et al.</em>: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details.</p> <p>The format of the data is described below.</p> <p>The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity.</p> <p>Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers.</p> <p>The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below.</p> <p>The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory.</p> <p>The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following:</p> <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul> <p> </p>
Data and analysis scripts for the OSDI 2018 paper "Taming Performance Variability"
<p>This repository contains our raw datasets and notebooks for analyzing performance results of benchmarks executed on CloudLab machines.</p> <p>File organization:</p> <ul> <li><code>notebooks/</code> - Various Jupyter notebooks containing our analysis. Contains analysis on our main dataset as well as some analysis on one-off collections. <ul> <li><code>bench-lib/common.py</code> - Common python utilities that are shared by the notebooks.</li> <li><code>disk-process.ipynb</code> - Main notebook for analysis of disk performance (both HDDs and SSDs).</li> <li><code>e-vs-cov.ipynb</code> - Analysis of E vs CoV for selected configurations.</li> <li><code>env-test.ipynb</code> - Version information for Python and installed packages.</li> <li><code>kernel-testing-Nd.ipynb</code> - Analysis of data using kernel two-sample testing in one and multiple dimensions..</li> <li><code>mem-process.ipynb</code> - Main notebook for analysis of memory performance.</li> <li><code>network-process.ipynb</code> - Main notebook for analysis of network performance.</li> <li><code>normality.ipynb</code> - Notebook for the high-level analysis of normality (or lack of it) in the collected data.</li> <li><code>quantile-regression.ipynb</code> - Notebook for Quantile Regression analysis for both disk and memory data.</li> <li><code>temporal-analysis.ipynb</code> - Notebook to search for/analyze any temporal aspects to our dataset.</li> <li><code>testbed-coverage.ipynb</code> - Analysis of the extent to which we were able to cover all of the test hardware.</li> <li><code>variability.ipynb</code> - Notebook for the high-level analysis of variability.</li> <li><code>wisc-disktests.ipynb</code> - <strong>ONE-OFF RUN</strong>: Analyzing variation in fio results on Wisconsin SSDs over repeated runs.</li> <li><code>wisc-memtests.ipynb</code> - <strong>ONE-OFF RUN</strong>: Analysis of benchmark order for various Wisconsin hardware/memory configurations.</li> </ul> </li> <li><code>data/</code>. Contains a dump of the dataset (as of 04/04/2018). This dataset is generated by running the code contained in the <a href="https://gitlab.flux.utah.edu/emulab/cloudlab-orchestration">https://gitlab.flux.utah.edu/emulab/cloudlab-orchestration</a> repository. Some additional one-off collections are contained here as well. <ul> <li><code>CoV-Summary/</code> - Data derived from our raw dataset for use by <code>notebooks/e-vs-cov.ipynb</code>.</li> <li><code>nodes/</code> - Simple list of all nodeids for each hardware type for use in <code>notebooks/testbed-coverage.ipynb</code>.</li> <li><code>raw-data/</code> - Raw .sql (and .csv files made from it) file containing a dump of our primary dataset through April 4th, 2018, used for the majority of the notebooks. Filtering is done in our notebooks to exclude data past April 1st, 2018 (as well as remove runs that appear to contain impossible execution conditions).</li> <li><code>wisc-disktests/</code> - <strong>ONE-OFF RUN</strong>: Results from successive runs of fio on Wisconsin SSDs. Used in <code>notebooks/wisc-disktests.ipynb</code>.</li> <li><code>wisc-memtests/</code> - <strong>ONE-OFF RUN</strong>: Results from memory benchmarks run with specific orderings on various Wisconsin machines. Used in <code>notebooks/wisc-memtests.ipynb</code>.</li> <li><code>wisc-pagemaps/</code> - <strong>ONE-OFF RUN</strong>: Virtual-to-Physical memory address mappings for tests run in <code>wisc-memtests/</code>. Used in <code>notebooks/wisc-memtests.ipynb</code>.</li> </ul> </li> </ul>
Research data supporting "Single particle automated raman trapping analysis"
<p>Research raw data supporting the publication:</p> <p>Penders J., et al., Nature Communications. (2018) 9:4256 | DOI: 10.1038/s41467-018-06397</p>
Data for "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain"
<p>Data for the "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain".</p> <p>All files except 'simulated_hypoxia.csv' contains both input and output data.</p>
Data, analysis scripts, simulations, and schematics files for "Position Sensitive Alpha Detector for an Associate Particle Imaging System"
<p>Measured data and analysis script, as well as, simulated data, input scripts, as well as, schematics for the readout board for our publication "Position Sensitive Alpha Detector for an Associate Particle Imaging System"</p>
Impact of algorithm choice in morphological phylogenetic analysis with inapplicable data
<p>Files to accompany Brazeau <em>et al</em>. (2019), describing the impact of using our new algorithm for handling inapplicable data.</p> <p>Details of the datasets analysed are give at <a href="https://ms609.github.io/ExploreInapplicable/r_dataset_details.html">ms609.github.io/ExploreInapplicable/r_dataset_details.html</a></p>
Statistical analysis of chlorate occurrence data in food
<p>In accordance with Article 29 (1) (a) of Regulation (EC) No 178/2002, the European Commission asked the European Food Safety Authority (EFSA) in 2014 for a scientific opinion on the risks for human health related to the presence of chlorate in food from all sources, taking also into account its presence in drinking water. The opinion found that “Chronic exposure of adolescent and adult age classes did not exceed the TDI. However, at the 95th percentile the TDI was exceeded in all surveys in ‘Infants’ and ‘Toddlers’ and in some surveys in ‘Other children’. Chronic exposures are of concern in particular in younger age groups with mild or moderate iodine deficiency.” Food manufacturers have started to optimise their manufacturing processes to lower chlorate residue level in foods and the European Commission in 2017 provided a revised Guidance document on good hygiene practices regarding the use of chlorinated disinfectant. It can therefore be expected that chlorate levels in foods are now lower compared with the levels found in the samples from 2011 to 2014. The European Commission (EC) requested EFSA in 2018 to provide an updated statistical analysis on chlorate occurrence levels in foods. A set of 14 Excel tables containing the statistical analysis of reported results for the analysis of chlorates from pesticides monitoring and contaminants monitoring programmes have been prepared. The data is presented at three levels of aggregation using FoodEx product categories. Analysis was performed for two time points, the 2011-2017 dataset contained 15,741 valid results and the 2015-2017 dataset contained 28,033 valid results. Mean, median and percentile (75th, 90th, 95th) for lower bound, middle bound and upper bound concentration values were calculated. Caution should be applied to percentile values calculated from a limited number of results.</p>
Code4Bench: A Multidimensional Benchmark of Codeforces Data for Different Program Analysis Techniques
<p>Reproducible research relies on well-designed benchmarks. However, evaluation on a single benchmark increases the risk of overfitting; that is, an optimization to reach a certain performance. In recent years several well-designed benchmarks have been constructed for different subfields of program analysis. However, they often involve real-world industrial projects in few languages such as C or Java. We provide Code4Bench, a benchmark comprising 3,421,357 programs totaling of 306,053,105 lines of code in 41 versions of 28 programming languages such as C/C++, Java, Python, and Kotlin. We have constructed this benchmark from Codeforces, a famous programming competition website, which is widely used by international programmers. Code4Bench advances the state-of-the-art in conducting reproducible and comparative experiments. It helps mitigate the bias and increase the generality and conclusiveness of the results. We present our methodology in construction of Code4Bench and give various descriptive statistics. We have also conducted an online survey on the users of Codeforces’ website whose code is included in the benchmark. The survey is concerned about the user’s demographic information and programming habits, whose results are also provided in the benchmark. Finally, we leveraged an automatic process by which we localized faults within the faulty versions and categorize them according to a coarse-grained classification. In addition to its usage in empirical studies, Code4Bench can be used to teach programming and evolve algorithmic problems. We release Code4Bench in database format to allow researchers to extract other data of the benchmark by arbitrary queries.</p> <p>Code4Bench version 1.0.0 is publicly available at <a href="https://zenodo.org/record/2582968">https://zenodo.org/record/2582968</a>, with DOI 10.5281/zenodo.2582968, thereby providing long-term storage and versioning. It is released under the terms of Creative Commons Attribution 4.0 International license. Code4Bench is also publicly available at: <a href="https://github.com/code4bench/Code4Bench">https://github.com/code4bench/Code4Bench</a>, in which we have provided some additional information and script examples.</p>
A Meta-Analysis on the Reliability of Comparative Judgement Data
<p>This is the data and R analysis script with the article "A Meta-Analysis on the Reliability of Comparative Judgement"</p>
Supporting data for: "Diaphysator: an online application for the exhaustive cartography and user-friendly statistical analysis of long bone diaphyses"
<p>Example of dataset to be used with the R-shiny application “Diaphysator”, composed of right tibiae and femora.</p> <p>These data file have been published in: Lacoste Jeanson, A., Santos, F., Villa, C., Banner, J., & Bruzek, J. (2018). Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT. <em>American Journal of Physical Anthropology</em>, 167, 813– 826. doi: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23713">10.1002/ajpa.23713</a></p> <p>This zip file contains:</p> <ul> <li>an “Information file” in CSV format</li> <li>various data files for human femora and tibiae in CSV format</li> </ul> <p>For all CSV files, the field separator is the comma “,” and the character used for decimal points is the dot “.”</p>
Feature count data for Love et al. 2019 analysis for "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper
<p>Feature count data for Love et al. 2019 analysis used in the "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper. For reproducing the analyses and figures using the <a href="https://github.com/Oshlack/ec-dtu-paper/">ec-dtu-paper</a> code.</p> <p>Contains:</p> <ul> <li>Equivalence class count matrix for all 24 samples (using counts from Salmon)</li> <li>Salmon quantification results for all 24 samples</li> <li>Exon counts for all 24 samples using DEXSeq-count</li> </ul>
Synthetic Data for Neutrophil Analysis: Sets with irregular shapes and Poisson noise
<p><strong>Synthetic Datasets with irregular shapes and Poisson noise.</strong></p> <p><strong>Part of the PhagoSight neutrophil tracking and analysis package (Henry, et al., PLOS ONE, 2013):</strong></p> <p> </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636</p> <p>http://www.phagosight.org</p> <p>https://github.com/phagosight/phagosight</p> <p> </p> <p>A series of synthetic data sets that reproduce different behaviour characteristics of migrating neutrophils were generated in MATLAB. The data sets consisted of six artificial neutrophils that travelled along paths that presented different conditions of tortuosity, times to activation and proximity to other neutrophils during 98 time frames.</p> <p>Numerous data sets of neutrophils in zebrafish were carefully observed before setting the characteristics. Six trajectories were manually determined by setting the row, column positions of the centroids at every time point for 98 time frames. Each trajectory was designed so that it would represent different neutrophil behaviours: some trajectories were very oriented and had movements with uniform distance between time frames, whilst others were less uniform and would move at different velocities, some were tortuous whilst others were straight. The trajectories of cells 1 and 2 collided several times in the second half of the time frames whilst cells 3 and 4 collided at the beginning of the movement. Cell 6 migrated without meandering and then stopped at the end (which represents the wound area of an inflammation-based experiment) whilst 5 presented a delayed activation. </p> <p>Each time frame consisted of 11 slices of z-stack each with 275 x 275 pixels, where the neutrophils were formed by <strong>irregular shapes </strong>(sum of Gaussians) and <strong>Poisson Noise</strong> (check the corresponding sets with regular shapes, i.e. Gaussians with Gaussian noise plus another set with a <strong>single large neutrophil</strong> and Poisson noise) distributions of higher intensities than the background. The orientation of the Poisson varied according to the displacement of the artificial neutrophils, <em>i.e.</em>they were round when the cells were static, or elongated when in movement. The tracks with the Shapes were saved as the <em>gold standard</em> and five different data sets were generated by adding varying levels of white Poisson noise resulting in data sets with distributions with increasing similarity between the neutrophils and the background reflected by the decreasing values of the Bhattacharyya Distance (1.61, 1.25, 1, 0.66, 0.45) as defined by Coleman 1979.</p> <p> </p> <p>Files corresponding to the sets with irregular shapes and Poisson noise (noise increases from 1 to 5):</p> <ul> <li><strong> x,y,t trajectories ThreeDTracks</strong></li> <li><strong> Ground Truth syntheticData_P_mat_La </strong></li> <li><strong> First data set syntheticData_P1_mat_Re</strong></li> <li><strong> Second data set syntheticData_P2_mat_Re</strong></li> <li><strong> Third data set syntheticData_P3_mat_Re</strong></li> <li><strong> Fourth data set syntheticData_P4_mat_Re</strong></li> <li><strong> Fifth data set syntheticData_P5_mat_Re</strong></li> </ul> <p>Corresponding GIF files are also included as illustrations of the cells in motion.</p> <p> </p> <p>Main Reference:</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636"><strong><em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model</strong> </a><br> Henry KM, Pase L, Ramos-Lopez CF, Lieschke GJ, Renshaw SA, Reyes-Aldasoro CC. (2013) <em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model. PLOS ONE 8(8): e72636. <a href="https://doi.org/10.1371/journal.pone.0072636">https://doi.org/10.1371/journal.pone.0072636</a></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.