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701 results for “factor analysis”
Monthly fluorescence parallel factor analysis (PARAFAC) components for Shark River Slough, Taylor Slough, and Florida Bay, Everglades National Park (FCE LTER), Florida, USA, April 2011 - ongoing
Dissolved organic matter plays an important role in biogeochemical processes in aquatic environments such as elemental cycling, microbial loop energetics, and the transport of materials across landscapes. Since most of N (> 90%) and P (around 90%) is in the organic form in the oligotrophic subtropical Florida Coastal Everglades (FCE), study of the source and dynamics of dissolved organic matter (DOM) in the ecosystem is crucial for the better understanding of the biogeochemical cycling of nutrients. FCE are composed of estuaries with distinct regions with different biogeochemical processes. Freshwater marsh primarily receives terrestrial input and local autochthonous vegetation production. Mangrove ecotone, nevertheless, is affected by the tidal contributions from Florida Bay and local mangrove production. Florida Bay (FB) is a wedge-shaped shallow oligotrophic estuary which lays south of the Everglades, the bottom of which is covered with a dense biomass of seagrass. The sources of both freshwater and nutrients in FCE are difficult to quantify, owing to the non-point source nature of runoff from the Everglades and the dendritic cross channels in the mangroves. Furthermore, the combination of multiple DOM sources (freshwater marsh vegetation, mangroves, phytoplankton, seagrass, etc.), and the potential seasonal variability of their relative contribution, along with the history of (photo)chemical and microbial diagenetic processing, and complex advective circulation, makes the study of DOM dynamics in FCE particularly difficult using standard schemes of estuarine ecology. Quantitative information of DOM is very useful to investigate the biogeochemical cycling of DOM to a certain degree, however, qualitative information is necessary to better understand the source and dynamics of DOM. Since fluorescence spectroscopic techniques are very sensitive, quick and simple, they have been applied to investigate the fate of DOM in estuaries. Here, we have quantified a series of
Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators (Dataset)
<p>Datafiles of the article "Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators"</p>
Landslide Formation Factors Analysis for the Transcarpathia_Ukraine
<p>Spatial patterns of landslides occurrence within the Transcarpathian region using GIS tools were evaluated. In order to identify the main and derived geological factors that determine the spread and activation of landslides within the region, 2575 landslides were analyzed, with a total area of 360,576 km square. The factors were represented by clams constructed in ArcView Spatial Analyst: terrain and its derivatives (slope angles, spatial orientation of the slopes, dispersion (mean deviation) of the terrain, the trend of the terrain and its local component); density of structural-tectonic heterogeneities. It is established that the maximum of landslides development is at altitudes with gypsometric marks of 280-730 m, slopes with a slope of 7.5-22.4 °, which are oriented to the west, southwest, south and southeast and up to 500 m to watercourses. Two-thirds of all landslides investigated are within a kilometer zone along structural-tectonic disturbances and at distances of up to 1250 m of disturbances having an azimuth of 90-180°. The applied approach, for the first time made it possible to establish patterns of landslides occurrence based on the results of a large array of initial cartographic information processing (not limited by certain a priori genetic and/or theoretical interpretations) and obtain reliable limit values for characterizing landslides formation. As a result, based on mapping of areas with characteristic values of established six landslide formation factors, a landslides forecasting map was received.</p>
Data associated with the article 'Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis'
<p>The efficacy of oral language comprehension interventions varies, but the reasons for this variation have received little attention. A meta-analysis was conducted to examine intervention factors associated with the efficacy (as expressed with effect sizes) of oral language comprehension interventions in children under the age of 18 with or at risk for (Developmental) Language Disorder, (D)LD.</p> <p>The meta-analysis article together with this additional material comprise the content needed for a thorough understanding and replication of the results.</p> <p>This dataset is based on two systematic scoping reviews on oral language comprehension interventions (Tarvainen et al., 2020, 2021). Further information from the sourced articles was extracted for this study titled ‘Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis’. </p> <p>In the future, we hope that this data is used with a growing body of oral language comprehension interventions to conduct further and more detailed examinations of intervention factors associated with efficacy.</p> <p>References:</p> <p>Tarvainen, S., Launonen, K., & Stolt, S. (2021). Oral language comprehension interventions in school-age children and adolescents with developmental language disorder: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>6</em>, 1–24. https://doi.org/10.1177/23969415211010423</p> <p>Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8-year-old children with language disorders or difficulties: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>5</em>, 1–24. https://doi.org/10.1177/2396941520946</p> <p> </p>
Supplementary Files for "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals"
<p>This repository hosts supplemental files for the Manuscript "Proteomic analysis of the sponge Aggregation Factor implicates an ancient toolkit for allorecognition and adhesion in animals" by Ruperti, et al., 2024.</p> <ul> <li><strong>Suppl_File_wreath_domain_model.pdb</strong>: AlphaFold3 model for the <em>C. prolifera</em> MAFp3 wreath domain (aa 33 - 317)</li> <li><strong>Suppl_File_MAFAP1_Cterm_model.cif</strong>: AlphaFold3 model for the <em>C. prolifera</em> MAFAP1 C-terminal domain, region 1 and 2</li> <li><strong>Suppl_File_AFInteracting_hmm.hmm</strong>: HMM sequence profile of AF-interacting region of C. prolifera proteins</li> <li><strong>XXX_Foldseek.zip</strong>: Foldseek raw search results, separated by target databases (Swissprot, AFDB, CATH50)</li> </ul>
Analysis accompanying "Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain"
<p>This repository documents the raw data processing and figure generation for the article “Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the <em>Drosophila </em>larval brain”, doi: 10.1261/rna.079552.122.</p>
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Dataset and analysis file for 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands
<p>Dataset and analysis using KEN-Q method for a 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands</p>
Figure 2 in Multivariate analysis of the effects of site factors on the distributions of raphignathoid mites (Acari: Raphignathoidea)
Figure 2. Total abundance values of the species according to habitat types (a), seasons (b) and elevation belts (c).
Figure 1 in Multivariate analysis of the effects of site factors on the distributions of raphignathoid mites (Acari: Raphignathoidea)
Figure 1. The frequency (1) and abundance (2) values of the species (a), genus (b), and families (c).
Plant‐eating carnivores: Multispecies analysis on factors influencing the frequency of plant occurrence in obligate carnivores
<p>Plant-eating behavior is one of the greatest mysteries in obligate carnivores. Despite unsuitable morphological and physiological traits for plant consumption, the presence of plants in scat or stomach contents has been reported in various carnivorous species. However, researchers' interpretations of this subject are varied, and knowledge about it is scarce, without any multispecies studies. This study assessed the extent of variation in the frequency of plant occurrence in scat and stomach contents, as well as its relationship with various factors in 24 felid species using data from 213 published articles. Since the frequency of plant occurrence has not always been reported, we created two-part models and estimated parameters in a Bayesian framework. We found a significant negative relationship between the frequency of plant occurrence and body mass. This may be because plant-eating behavior reduces the energy loss caused by parasites and increases the efficiency of energy intake, which has a greater importance in smaller animals that have relatively high metabolic rates. This exploratory study highlights the importance of considering plant consumption in dietary studies on carnivorous species to understand the adaptive significance of this behavior and the relationship between obligate carnivores and plants.</p>
Fig. 2 in Factors Influencing Weed Species Diversity In Southeastern Part Of Latvia: Analysis Of A Two-Year Weed Survey Data
Fig. 2. Influence of field variables and crop groups on weed density, common and rare species richness in 2013 and 2014. Constrained analysis (RDA) with five constraining variables: Year (2013 or 2014), N_group (N1 0-50, N2 50- 100, N3 100-140, N4>140 kg ha–1 pure nitrogen per hectar), pH and crop group (c.s. – spring cereals, c.w. – winter cereals, S o.s.r. – spring oilseed rape, W o.s.r. – winter oilseed rape, Other – other crops, including maize, grassland, root crops and legumes). The proportion of constrained variation was 33%, the overall analysis and each of the factors were significant (p <0.05), significance tested with permutation tests.
Fig. 1 in Factors Influencing Weed Species Diversity In Southeastern Part Of Latvia: Analysis Of A Two-Year Weed Survey Data
Fig. 1. Generalized linear models (Poisson) of the total species richness against species density in 2014 and 2013. Model coefficients were 2.41 (p <0.0001) in 2014 and 2.40 (p <0.0001) in 2013. Residual deviance / residual d.f. ratio was used to estimate model overdispersion (1.62 in 2014 and 1.35 in 2013).
Fig. 3 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 3. Correlation between average IF and uncitedness rate of journals on zoology in Sample 1 between 2006 and 2010. The highlighted represents the data for Neotropical Ichthyology.
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-Figure 2. Identified risk factors hierarchy
<p>AED - antiepileptic drug, CP - cerebral palsy, GDD - global developmental delay, GA - gestational age, BW- birth weight, RS - repeated/recurrent seizure, MD - type/mode of delivery, AS1 - Apgar score at 1 minute, AS5 - Apgar score at 5 minute, AS10 - Apgar score at 10 minute, SO - seizure onset, ST_EPI - status epilepticus, UBS - ultrasound brain scan, MSU- maternal substance used, MIS – maternal inflammatory state, PRM- prolonged rupture of membranes, PNN – postnatal neuroimaging, PNS – postnatal seizure. The most frequently identified risk factors were the EEG findings (abnormal / severe electroencephalogram results), seizure characteristics (type, onset, duration, semiology), etiology, birth weight, Apgar score, cerebral ultrasound scan findings (abnormal) (Figure 2).</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context
<p>R retrospective, P prospective, CC case – control study, MC multicenter controlled trail, PB populational based, HB hospital based, C clinical, CT computed tomographic scan, MRI cerebral magnetic resonance imaging, CUS cranial ultrasonography / cerebral ultrasound, USG ultrasonography, EEG electroencephalogram (standard), CpH cord Ph, BpH blood Ph, HT therapeutic hypothermia It can be noticed that seizure diagnosis was based on clinical grounds and functional explorations naming neuroimaging and/or EEG procedures (conventional EEG, aEEG, vEEG, CUS, MRI). The minimum number of newborns considered in these studies was 55, while the maximum was 403 with a mean of 148 (SD=86.75, median=112, IQR: (98,175)) and a total of 2226 evaluated cases.</p>
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
Dataset for meta-analysis "The motherhood penalty's size and factors"
<p><strong>PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205. <a href="https://doi.org/10.3897/popecon.8.e121438" target="_blank" rel="noopener">https://doi.org/10.3897/popecon.8.e121438</a></strong></p> <p> </p> <p><strong>Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".</strong></p> <p><strong>The data is presented in WORD format.</strong></p> <p> </p> <p><strong>Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".</strong></p> <p><strong>The data is presented in EXCEL format. </strong></p> <p><strong>Description of table headers:</strong></p> <p>estimate_number - Number of the estimate</p> <p>paper_number - Number of the paper</p> <p>paper_name - Paper (year and first author)</p> <p>paper_excluded - Paper was excluded from the final sample</p> <p>survey - Data source</p> <p>table_in_paper - Number of the table with the regression results in the paper</p> <p>coeff - Regression coefficient for parenthood variable (estimate)</p> <p>se - SE of the estimate</p> <p>t - t-value of the estimate</p> <p>ols - Estimate is obtained using the OLS method</p> <p>fixed_effects ­- Estimate is obtained using the fixed effects method</p> <p>panel - Model considers panel data (for several years)</p> <p>quintile - Estimate is obtained using the quintile regression method</p> <p>other - Estimate is obtained using other methods</p> <p>selection_into_motherhood - Estimate is obtained allowing for selection into motherhood</p> <p>hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)</p> <p>annual_earnings - Annual earnings are considered in the model</p> <p>monthly_wage - Monthly wage is considered in the model</p> <p>daily_wage - Daily wage is considered in the model</p> <p>hourly_wage - Hourly wage is considered in the model</p> <p>min_age_kid - Child's age (minimum)</p> <p>max_age_kid - Child's age (maximum)</p> <p>motherhood - Model uses a dummy variable of the presence of children</p> <p>num_kids - Model uses a variable of the number of children</p> <p>kid1 - Model uses a variable of the presence of one child</p> <p>kid2p - Model uses a variable of the presence of two or more children</p> <p>kid2 - Model uses a variable of the presence of two children</p> <p>kid3p - Model uses a variable of the presence of three or more children</p> <p>kid3 - Model uses a variable of the presence of three children</p> <p>kid4p - Model uses a variable of the presence of three or more children</p> <p>race/nationality - Model includes a race/ethnicity variable</p> <p>age - Model includes the age variable</p> <p>marstat - Model includes the marital status variable</p> <p>oth_char_hh - Model includes any other variables of other household characteristics</p> <p>settl_type - Model includes a variable of the type of settlement (urban, rural)</p> <p>region - Model includes a variable of the region of the country</p> <p>education - Model includes information on the level of education</p> <p>experience - Model includes a variable of work experience</p> <p>pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education</p> <p>tenure - Model includes a variable of the duration of employment at the current job</p> <p>interruptions - Model includes a variable of employment interruptions (related to motherhood)</p> <p>occupation - Model includes an occupation variable</p> <p>industry - Model includes a variable of the industry of employment</p> <p>union - Model includes a variable of trade union membership</p> <p>friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).</p> <p>hours - Model includes a variable of the number of hours worked</p> <p>sector - Model includes a variable of the type of employer ownership (public or private)</p> <p>informal - Model includes a variable of informal employment</p> <p>size_ent - Model includes a variable of the employer size</p> <p>min_age_woman - Woman's age (minimum)</p> <p>max_age_woman - Woman's age (maximum)</p> <p>mean_age_woman - Woman's age (mean)</p> <p>restricted - Sample is limited</p> <p>private - Model considers only private sector employees</p> <p>state - Model considers only public sector employees</p> <p>full_time - Model considers only full-time workers</p> <p>part_time - Model considers only part-time workers</p> <p>better_educated - Model considers only women with a high level of education</p> <p>lower_educated - Model considers only women with a low level of education</p> <p>married - Model includes only married women</p> <p>single - Model includes only single women</p> <p>natives - Model includes only native women (born in the country)</p> <p>immigrants - Model includes only immigrant women (born abroad)</p> <p>race - Model includes only women of a particular race</p> <p>min_year - Time period (minimum year)</p> <p>max_year - Time period (maximum year)</p> <p>journal - Type of publication</p> <p>usa - Sample includes women from the USA</p> <p>western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)</p> <p>north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)</p> <p>south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)</p> <p>east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)</p> <p>china - Sample includes women from China</p> <p>Russia - Sample includes women from Russia</p> <p>others - Sample includes women from other countries</p> <p>country - Country name</p>
Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis
<p>Dataset used in <em>Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis </em>[<a href="https://arxiv.org/abs/2211.11784">arXiv: <strong>2211.11784</strong></a>]. This dataset will be helpful to validate different continuum prediction model and study absorption systems.<br> <br> The descriptions of individual files can be found here:</p> <ul> <li><a href="/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-dr16.tar.gz?versionId=427c1aca-6f12-4e84-b91e-f9a3d678329e">sdss-dr16.tar.gz</a> : continuum prediction for ~100,000 quasar spectra from SDSS DR16, see Section 3.1 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-with-perturb.tar.gz?versionId=55bc466d-4cbf-43ba-9b83-209ffba4ec30">sdss-mock-with-dla-with-perturb.tar.gz </a> : ~150,000 mock quasar spectra to validate QFA performance with perturbation on quasar continuum from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-without-perturb.tar.gz?versionId=efa5d05a-2a40-4a43-801e-c95b29a4efb2">sdss-mock-with-dla-without-perturb.tar.gz</a>: ~150,000 mock quasar spectra to validate QFA performance with quasar continuum directly from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>.<br> <br> <br> </li> </ul>
Meta-analysis and critical review of trophic discrimination factors (Δ13C and Δ15N): importance of tissue, trophic level, and diet source
<ol> <li>Robustly quantifying dietary resource use and trophic position using stable isotopes requires accurate trophic discrimination factors (TDF; Δ<sup>13</sup>C and Δ<sup>15</sup>N for carbon and nitrogen, respectively), defined as the isotopic difference between consumer and diet. Early TDF studies converged on values of around 1.0‰ for Δ<sup>13</sup>C and 3.4‰ for Δ<sup>15</sup>N, but more recent work indicates that TDF values may be more nuanced, depending on taxa, tissues, trophic level, and diets. Yet, the relative importance of these factors remains unclear.</li> <li>Focusing on vertebrates (birds, fish, herptiles, and mammals), we conducted a literature review of 279 studies that estimated TDF values and used a Bayesian framework to determine how tissue type, trophic level, and diet source influence variation in Δ<sup>13</sup>C and Δ<sup>15</sup>N. Additionally, we reviewed 358 trophic ecology studies to determine if studies accounted for these factors during their TDF selection process.</li> <li>For Δ<sup>13</sup>C, vertebrates showed consistent patterns among tissue types (likely influenced by amino acid composition) and between trophic levels and diet sources (likely a result of dietary protein content and metabolic routing). Comparatively, for Δ<sup>15</sup>N, vertebrates showed considerable variation among tissue types and trophic levels, likely due to differences in tissue synthesis and physiological capabilities. Overall, Δ<sup>13</sup>C ranged from -5.1‰ to 9.1‰ and Δ<sup>15</sup>N from -3.3‰ to 9.7‰, underscoring that 1.0‰ for Δ<sup>13</sup>C and 3.4‰ for Δ<sup>15</sup>N are not universally appropriate. Moreover, both Δ<sup>13</sup>C and Δ<sup>15</sup>N varied by more than 9‰ within a single species and tissue type, demonstrating that using TDF values from the same, or similar, species may not be appropriate if diet and trophic level are not considered.</li> <li>Despite the importance of diet source on TDF values, most trophic ecology studies did not account for it. Further, most fish studies relied on literature review values that failed to account for tissue type, trophic level, and diet source. To aid ecologists in diet and trophic assessments of vertebrates, we used our meta-analysis to model taxon-specific TDF estimates (mean ± SD) for each tissue type, trophic level, and diet source combination. These more refined TDF values should improve ecological assessments that use stable isotopes.</li> </ol>
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.