Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,456
datasets available to search
ShareScore release 0.7.1
Dataset results
1,456 results for “parallelism”
A Reconfiguration Algorithm for Power-Aware Parallel Applications
<p><strong><em>Abstract: </em></strong><em>In current computing systems, many applications require guarantees on their maximum power consumption to not exceed the available power budget. On the other hand, for some applications, it could be possible to decrease their performance, yet maintaining an acceptable level, in order to reduce their power consumption. To provide such guarantees, a possible solution consists in changing the number of cores assigned to the application, their clock frequency and the placement of application threads over the cores. However, power consumption and performance have different trends depending on the application considered and on its input. Finding a configuration of resources satisfying user requirements is in the general case a challenging task. In this paper we propose Nornir, an algorithm to automatically derive, without relying on historical data about previous executions, performance and power consumption models of an application in different configurations. By using these models, we are able to select a close to optimal configuration for the given user requirement, either performance or power consumption. The configuration of the application will be changed on-the-fly throughout the execution to adapt to workload fluctuations, external interferences and/or application's phase changes. We validate the algorithm by simulating it over the applications of the PARSEC benchmark suite. Then, we implement our algorithm and we analyse its accuracy and overhead over some of these applications on a real execution environment. Eventually, we compare the quality of our proposal with that of the optimal algorithm and of some state of the art solutions.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p> <p> </p>
High-Level and Efficient Stream Parallelism on Multi-core Systems with SPar for Data Compression Applications
<p>The stream processing domain is present in several real-world applications that are running on multi-core systems. In this paper, we focus on data compression applications that are an important sub-set of this domain. Our main goal is to assess the programmability and efficiency of domain-specific language called SPar. It was specially designed for expressing stream parallelism and it promises higher-level parallelism abstractions without significant performance losses. Therefore, we parallelized Lzip and Bzip2 compressors<br> with SPar and compared with state-of-the-art frameworks. The results revealed that SPar is able to efficiently exploit stream parallelism as well as provide suitable abstractions with less code intrusion and code re-factoring.</p>
Raw data for figures used in manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure. </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p> - Folders for the different experiments with the name expt[1..30]</p> <p> - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p> - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p> - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p> - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p> - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - README.txt</p> <p>3. fig5.tar.gz</p> <p> - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p> - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary08.tar.gz</p> <p> - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart12.tar.gz</p> <p> - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p> - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p> - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p> - README.txt (information on the image processing tutorial). </p>
Data for "Fully Implicit Time Stepping can be Efficient on Parallel Computers"
<p>Benchmark data and python plotting programs</p>
Supplementary Data for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting
<p>This data repository is provided as a supplement to the paper *Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* written by Håvard Heitlo Holm, Martin Lilleeng Sætra and Peter Jan van Leeuwen. It contains the complete datasets (initial conditions and results of the ensemble simulations) obtained from the experiments presented therein.</p> <p>This data set is generated by, and can be further post-processed and visualized by, the code published as *metno/gpu-ocean: Supplementary Software for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* by Håvard Heitlo Holm, Martin Lilleeng Sætra and André Rigland Brodtkorb (DOI 10.5281/zenodo.3458291). </p> <p> </p>
The netCDF output data of Parallel Princeton Ocean Model based on OpenACC
<p>This dataset represents the output results from the simulated seamount case, where the outputs vary depending on whether parallel (p) or serial (s) execution is used, as well as the different simulation durations and resolutions applied.</p>
Current and Voltage for a Series-Parallel Configurations of Piezoelectric Transducers Dataset
<p>The piezoelectric elements used are arranged in two test configurations for the experiments: a series electrical connection of two units and a parallel electrical connection of two units, while remaining mechanically isolated. It is important to emphasize that although the elements are electrically connected, they are not mechanically coupled. Data collection focuses on two input variables—oscillation frequency and load resistance—with the output being the voltage across the piezoelectric elements and the current through the load. The experiments involve frequency sweeps from 0 to 200 Hz in 1 Hz increments and load resistance sweeps, fixed at 5 k<span><span>Ω</span></span> and ranging from 10 k<span><span>Ω</span></span> to 300 k<span><span>Ω</span></span>.</p> <div> <div> <div> <div> <p><span>The currents and voltages obtained from the measurement of the piezoelectric elements are included, as well as the frequency and resistance values. Finally, a test.m file is included in which the data from one of the sets can be viewed.</span></p> </div> </div> </div> </div>
A Proportional Control Strategy for Stiffness Tuning of Parallel Manipulators
<p>MBDyn models for the paper "A Proportional Control Strategy for Stiffness Tuning of Parallel Manipulators"</p>
Data from: Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex
<p>The brain predicts regularities in sensory inputs at multiple complexity levels, with neuronal mechanisms that remain elusive. Here, we monitored auditory cortex activity during the local-global paradigm, a protocol nesting different regularity levels in sound sequences. We observed that mice encode local predictions based on stimulus occurrence and stimulus transition probabilities, because auditory responses are boosted upon prediction violation. This boosting was due to both short-term adaptation and an adaptation-independent surprise mechanism resisting anesthesia. In parallel, and only in wakefulness, VIP interneurons responded to the omission of the locally expected sound repeat at sequence ending, thus providing a chunking signal potentially useful for establishing global sequence structure. When this global structure was violated, by either shortening the sequence or ending it with a locally expected but globally unexpected sound transition, activity slightly increased in VIP and PV neurons respectively. Hence, distinct cellular mechanisms predict different regularity levels in sound sequences.</p>
Data from: Parallel Pleistocene amphitropical disjunctions in a parasitic plant and its host
PREMISE OF THE STUDY: Aphyllon is a clade of holoparasites that includes closely related North American and South American species parasitic on Grindelia. Both Aphyllon (Orobanchaceae) and Grindelia (Asteraceae) have amphitropical disjunctions between North America and South America; however, the timing of these patterns and the processes to explain them are unknown. METHODS: Chronograms for the Orobanchaceae and Grindelia and their relatives were constructed using fossil and secondary calibration points, one of which was based on the inferred timing of horizontal gene transfer from a papilionoid legume into the common ancestor of Orobanche and Phelipanche. Elevated rates of molecular evolution in the Orobanchaceae have hindered efforts to determine reliable divergence time estimates in the absence of a fossil record. However, using a horizontal gene transfer event as a secondary calibration overcomes this limitation. These chronograms were used to reconstruct the biogeography of Aphyllon, Grindelia, and relatives using a DEC+J model implemented in RevBayes. KEY RESULTS: Aphyllon had two amphitropical dispersals from North America to South America, while Grindelia had a single dispersal. The dispersal of the Aphyllon lineage that is parasitic on Grindelia (0.40 Ma) took place somewhat after Grindelia began to diversify in South America (0.93 Ma). Using a secondary calibration based on horizontal gene transfer, we infer more recent divergence dates of holoparasitic Orobancheae than previous studies. CONCLUSIONS: Parallel host–parasite amphitropical disjunctions in Grindelia and Aphyllon illustrate one means by which ecological specialization may result in nonindependent patterns of diversity in distantly related lineages. Although Grindelia and Aphyllon both dispersed to South America recently, Grindelia appears to have diversified more extensively following colonization. More broadly, recent Pleistocene glaciations probably have also contributed to patterns of diversity and biogeography of temperate northern hemisphere Orobancheae. We also demonstrate the utility of using horizontal gene transfer events from well-dated clades to calibrate parasite phylogenies in the absence of a fossil record.
Data from: Annual and perennial Medicago show signatures of parallel adaptation to climate and soil in highly conserved genes
<p class="AbstractSummary">Human induced environmental change may require rapid adaptation of plant populations and crops, but the genomic basis of environmental adaptation remain poorly understood. We analyzed polymorphic loci from the perennial crop <i>Medicago sativa </i>(alfalfa or lucerne) and the annual legume model species <i>M. truncatula </i>to search for a common set of candidate genes that might contribute to adaptation to abiotic stress in both annual and perennial <i>Medicago</i> species.</p> <p class="AbstractSummary">We identified a set of candidate genes of environmental adaptation associated with environmental gradients along the distribution of the two <i>Medicago</i> species. Candidate genes for each species were detected in homologous genomic linkage blocks using genome-environment (GEA) and genome-phenotype association analyses.</p> <p>Hundreds of GEA candidate genes were species-specific, of these, 13.4% (<i>M. sativa</i>) and 24% (<i>M. truncatula</i>) were also significantly associated with phenotypic traits. A set of 168 GEA candidates were shared by both species, which was 25.4% more than expected by chance. When combined, they explained a high proportion of variance for certain phenotypic traits associated with adaptation. Genes with highly conserved functions dominated among the shared candidates and were enriched in Gene Ontology terms that have shown to play a central role in drought avoidance and tolerance mechanisms by means of cellular shape modifications and other functions associated with cell homeostasis.</p> <p class="AbstractSummary">Our results point to the existence of a molecular basis of adaptation to abiotic stress in <i>Medicago</i> determined by highly conserved genes and gene functions. We discuss these results in light of the recently proposed omnigenic model of complex traits.</p>
Parallel and non-parallel divergence within polymorphic populations of brook stickleback, Culaea inconstans (Actinopterygii: Gasterosteidae)
<p><span><span><span><span><span><span><span><span><span><span><span>Studying parallel evolution allows us to draw conclusions about the repeatability of adaptive evolution. Whereas populations likely experience similar selective pressures in similar environments, it is not clear if this will always result in parallel divergence of ecologically relevant traits. Our study investigates the extent of parallelism associated with the evolution of pelvic spine reduction in brook stickleback populations. We find that populations with parallel divergence in pelvic spine morphology do not exhibit parallel divergence in head and body morphology but do exhibit parallel divergence in diet. In addition, we compare these patterns associated with pelvic reduction in brook stickleback to well-studied patterns of divergence between spined and unspined threespine stickleback. Whereas spine reduction is associated with littoral habitats and a benthic diet in threespine stickleback, spine reduction in brook stickleback is associated with a planktonic diet. Hence, we find that pelvic spine divergence is associated with largely non-parallel ecological consequences across species.</span></span></span></span></span></span></span></span></span></span></span></p>
Hubble Frontier Field Clusters and their Parallel Fields: Photometric and Photometric Redshift Catalogs
<p>Source catalogs of the Hubble Frontier Field clusters and parallel fields. If used, please cite https://ui.adsabs.harvard.edu/abs/2021arXiv210301952P/abstract</p>
Adding the third dimension to studies of parallel evolution of morphology and function: an exploration based on parapatric lake-stream stickleback
Recent methodological advances have led to a rapid expansion of evolutionary studies employing three-dimensional landmark-based geometric morphometrics (GM). GM methods generally enable researchers to capture and compare complex shape phenotypes, and to quantify their relationship to environmental gradients. However, some recent studies have shown that the common, inexpensive, and relatively rapid two-dimensional GM methods can distort important information and produce misleading results because they cannot capture variation in the depth (Z) dimension. We use micro-CT scanned threespine stickleback (Gasterosteus aculeatus Linnaeus, 1758) from six parapatric lake-stream populations on Vancouver Island, British Columbia, to test whether the loss of the depth dimension in 2D GM studies results in misleading interpretations of parallel evolution. Using joint locations described with 2D or 3D landmarks, we compare results from separate 2D and 3D shape spaces, from a combined 2D-3D shape space, and from estimates of biomechanical function. We show that, although shape is distorted enough in 2D projections to strongly influence the interpretation of morphological parallelism, estimates of biomechanical function are relatively robust to the loss of the Z dimension.
Optimizing parameters for using the parallel auditory brainstem response (pABR) to quickly estimate hearing thresholds
<p><b>Objectives: </b>Timely assessments are critical to providing early intervention and better hearing and spoken language outcomes for children with hearing loss. To facilitate faster diagnostic hearing assessments in infants, the authors developed the parallel auditory brainstem response (pABR), which presents randomly timed trains of tone pips at five frequencies to each ear simultaneously. The pABR yields high-quality waveforms that are similar to the standard, single-frequency serial ABR but in a fraction of the recording time. While well-documented for standard ABRs, it is yet unknown how presentation rate and level interact to affect responses collected in parallel. Furthermore, the stimuli are yet to be calibrated to perceptual thresholds. Therefore, this study aimed to determine the optimal range of parameters for the pABR and to establish the normative stimulus level correction values for the ABR stimuli.</p> <p><b>Design: </b>Two experiments were completed, each with a group of 20 adults (18 – 35 years old) with normal hearing thresholds (≤ 20 dB HL) from 250 to 8000 Hz. First, pABR electroencephalographic (EEG) responses were recorded for six stimulation rates and two intensities. The changes in component wave V amplitude and latency were analyzed, as well as the time required for all responses to reach a criterion signal-to-noise ratio of 0 dB. Second, behavioral thresholds were measured for pure tones and for the pABR stimuli at each rate to determine the correction factors that relate the stimulus level in dB peSPL to perceptual thresholds in dB nHL.</p> <p><b>Results:</b> The pABR showed some adaptation with increased stimulation rate. A wide range of rates yielded robust responses in under 15 minutes, but 40 Hz was the optimal singular presentation rate. Extending the analysis window to include later components of the response offered further time-saving advantages for the temporally broader responses to low frequency tone pips. The perceptual thresholds to pABR stimuli changed subtly with rate, giving a relatively similar set of correction factors to convert the level of the pABR stimuli from dB peSPL to dB nHL.</p> <p><b>Conclusions: </b>The optimal stimulation rate for the pABR is 40 Hz, but using multiple rates may prove useful. Perceptual thresholds that subtly change across rate allow for a testing paradigm that easily transitions between rates, which may be useful for quickly estimating thresholds for different configurations of hearing loss. These optimized parameters facilitate expediency and effectiveness of the pABR to estimate hearing thresholds in a clinical setting.</p>
Automated, high-throughput image calibration for parallel-laser photogrammetry
<p>This contains the data required to recreate the analyses in this paper. The code for performing the machine learning and image processing methods presented in the paper are available as supplemental files to the manuscript, and are also available at https://github.com/ejlevy/Photogrammetry_Coding_InterLaser_Distance.</p> <p>Paper abstract: <span>Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject's body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. </span></p>
WMT'16 Biomedical Translation Task - Scielo parallel datasets
<p>Parallel data from Scielo for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html).</p> <p>It contains parallel data for es/en, fr/en and pt/en.</p> <p>The documents were derived from the Scielo database (https://scielo.org/en/).</p>
Climatic similarity and genomic background shape the extent of parallel adaptation in Timema stick insects
<p>Evolution can repeat itself, resulting in parallel adaptations in independent lineages occupying similar environments. Moreover, parallel evolution sometimes, but not always, uses the same genes. Two main hypotheses have been put forth to explain the probability and extent of parallel evolution. First, parallel evolution is more likely when shared ecologies result in similar patterns of natural selection in different taxa. Second, parallelism is more likely when genomes are similar, because of shared standing variation and similar mutational effects in closely related genomes. Here we combine ecological, genomic, experimental, and phenotypic data with Bayesian modeling and randomization tests to quantify the degree of parallelism and its relationship with ecology and genetics. Our results show that the extent to which genomic regions associated with climate are parallel among species of <em>Timema</em> stick insects is shaped collectively by shared ecology and genomic background. Specifically, the extent of genomic parallelism decays with divergence in climatic conditions (i.e., habitat or ecological similarity) and genomic similarity. Moreover, we find that climate-associated loci are likely subject to selection in a field experiment, overlap with genetic regions associated with cuticular hydrocarbon traits, and are not strongly shaped by introgression between species. Our findings shed light on when evolution is most expected to repeat itself.</p>
A Scatter Search approach for the Parallel Row Ordering Problem
<p>Instance data and full results used in https://doi.org/10.1007/978-3-031-26504-4_40</p>
ClinSpEn Corpus: Parallel English-Spanish COVID-19 Clinical Cases, Terminology and Ontology Concepts
<p><strong>ClinSpEn Parallel Corpus Collection</strong></p> <p>This repository contains the <strong>complete</strong> <strong>ClinSpEn corpus collection</strong>, which was used for the <strong><a href="https://temu.bsc.es/clinspen">ClinSpEn shared task</a></strong> at Biomedical WMT 2022.</p> <p>ClinSpEn is a collection of <strong>Gold Standard EN-ES parallel corpora of different types of clinical data</strong>: case reports, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content. It includes development and test data translated by professional medical translators that can be used <strong>to train and benchmark clinical EN-ES machine translation systems</strong>. Additionally, monolingual background data is provided so that the systems' performance can be analyzed in unseen data.</p> <p>If you use this dataset, please cite:</p> <blockquote> <pre><code>inproceedings{biowmt22, title={Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports}, author={Neves, Mariana and Yepes, Antonio Jimeno and Siu, Amy and Roller, Roland and Thomas, Philippe and Navarro, Maika Vicente and Yeganova, Lana and Wiemann, Dina and Di Nunzio, Giorgio Maria and Vezzani, Federica and others}, booktitle={WMT22-Seventh Conference on Machine Translation}, pages={694--723}, year={2022} } </code></pre> </blockquote> <p><strong>Data Description</strong></p> <p>ClinSpEn proposes three different sub-tracks, each based on a different type of clinical data:</p> <p><em>1. Clinical Cases:</em></p> <p>Parallel EN-ES COVID-19 clinical case reports. The direction of this sub-track is EN>ES.</p> <p>The dataset’s case reports were carefully selected to cover a wide range of aspects related to the disease: different types of patients (children, adults, elderly and pregnant people, babies), different comorbidities (cancer, mental health issues, immunosuppressed patients) and symptomatology (mild and severe presentations, dermatologic, immunologic and psychiatric manifestations, thrombosis, ...). The reports were translated from English to Spanish by a professional medical translator on a first step and revised by a clinical expert on a second step.</p> <p>The sample (dev) set and test set are made up of parallel txt files (50 and 152 documents each, respectively), with the Spanish version having a “.es” extension and the English files having a “.en” extension. Each report has been parallelized so that every sentence’s line number corresponds to the same sentence’s line number in both languages.</p> <p>The background data (9,804 files) is made up of a TSV file with four columns: filename, document number, line number and English line. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.</p> <p>If you need to map the entries in the join test + background document provided in earlier versions, you may use the "clinspen_clinicalcases_test-set_filename_mapping.tsv" file.</p> <p><em>2. Clinical Terminology:</em></p> <p>Parallel EN-ES clinical terms extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions and translated and revised by professional medical translators. The direction of this sub-track is ES>EN.</p> <p>The sample (dev) set contains 7,000 terms as a tab-separated file (TSV), with the first column corresponding to English terms and the second column to Spanish terms.</p> <p>The test data (12,128 terms) is made up of a TSV file with three columns: term number, English term and Spanish term.</p> <p>The background data (201,890 terms) is made up of a TSV file with two columns: term number and Spanish term.</p> <p>The term number columns can be used to map the entries in the join test + background document provided in earlier versions.</p> <p><em>3. Ontology Concepts:</em></p> <p>Parallel EN-ES concepts extracted from various open biomedical ontologies and taxonomies and then manually translated by a professional medical translator. The direction of this sub-track is EN>ES.</p> <p>The sample (dev) data includes 400 concepts. The terms are presented as tab-separated file (TSV), with the first column corresponding to English terms and the second column to Spanish terms. The third column includes the term’s origin ontology and its correspondent ID (separated by an underscore), while the fourth one includes a link to the concept in OBO Library.</p> <p>The test data (1,789 concepts) is made up of a TSV file with five columns: term number, English term, Spanish term, ontology id and OBO library URL.</p> <p>The background data (299,408 concepts) is made up of a TSV file with four columns: term number, English term, ontology id and OBO library URL.</p> <p>The term number columns can be used to map the entries in the join test + background document provided in earlier versions.</p> <p> </p> <p><strong>Related Links:</strong></p> <ul> <li> <p>ClinSpEn website with more information: <a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> </li> <li> <p>WMT website: <a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> </li> </ul> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any question or suggestion, please contact us at the following addresses:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br> - Darryl Estrada (<darrylestrada97 [at] gmail [dot] com>)<br> - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p> </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.