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579 results for “Stratification”
Data from: Experiments with artificial nests provide evidence for ant community stratification and nest site limitation in a tropical forest
Ants are dominant in tropical forests and many species nest in hollow cavities. The manner in which species are vertically stratified in these complex habitats is not known, with lack of nest sites being proposed to limit ant populations. Here we assess ant community stratification and nest site limitation in a lowland rainforest in New Guinea using experimental addition of artificial bamboo nests of two cavity sizes (small: ~12 mm. large: ~32 mm diameter) placed at ground level, in the understorey, and in the canopy. We also conducted a pilot experiment to test the utility of nest translocation. Nests were checked for occupancy after 10 weeks and half of the occupied nests were then translocated between forest plots, while keeping same vertical position. Occupancy of small nests was much higher in the understorey and canopy than at ground level (~75% versus ~25%). Translocation was successful, as a majority of nests was inhabited by the same species before and after translocation and there was no impact of translocation to a different plot compared to the control, except for a reduction in colony size at ground level. Our experiment demonstrates a vertical stratification in community composition of ants nesting in hollow dead cavities and shows that these ants are more nest-site limited in the higher strata than at ground level. Use of small artificial cavities has great potential for future experimental studies, especially for those focused on arboreal ants, as occupancy is high and translocation does not negatively affect their colony size.
Data from: Vertical distribution of marine invertebrate larvae in response to thermal stratification in the laboratory
We investigated the effect of the presence of an experimentally generated thermocline on the vertical distribution of larval Strongylocentrotus droebachiensis, Asterias rubens and Argopecten irradians. Vertical distributions were recorded over 90 min in rectangular plexiglass thermocline chambers designed to regulate the temperature of a central observation compartment to the desired values. The temperature in the bottom water layer (B) and the temperature difference between layers (ΔT) were manipulated in an orthogonal design. We used, for S. droebachiensis: 4 levels of ΔT (0, 3, 6 and 12 °C) and 3 levels of B (3, 6 and 9 °C); for A. rubens: 3 levels ΔT (0, 6 and 12 °C) and 2 levels of B (6 and 12 °C); and for A. irradians: 3 levels of ΔT (0, 5 and 11 °C) and 2 levels of B (5 and 11 °C). The difference in temperature between water layers did not affect the vertical distribution of echinoderms consistently, while the distribution of A. irradians was limited to the bottom layer when any thermal stratification was present regardless of strength. Our results suggest that the vertical position of larvae of S. droebachiensis and A. rubens is related to the temperatures of the surface layer and that the presence alone or the steepness of the thermocline has less influence on their distribution. Consequently, in the field, echinoderm larvae would aggregate at the surface unless temperature extremes were encountered. In contrast, the position of A. irradians was limited to the bottom layer in the presence of a thermocline of at least 5 °C (the shallowest used in our study). Such thermoclines are common in a natural setting and could affect the vertical distribution and horizontal dispersal of larvae by acting as a barrier to vertical migration.
Data from: Contrasting taxonomic stratification of microbial communities in two hypersaline meromictic lakes
Hypersaline meromictic lakes are extreme environments in which water stratification is associated with powerful physicochemical gradients and high salt concentrations. Furthermore, their physical stability coupled with vertical water column partitioning makes them important research model systems in microbial niche differentiation and biogeochemical cycling. Here, we compare the prokaryotic assemblages from Ursu and Fara Fund hypersaline meromictic lakes (Transylvanian Basin, Romania) in relation to their limnological factors and infer their role in elemental cycling by matching taxa to known taxon-specific biogeochemical functions. To assess the composition and structure of prokaryotic communities and the environmental factors that structure them, deep-coverage small subunit (SSU) ribosomal RNA (rDNA) amplicon sequencing, community domain-specific quantitative PCR and physicochemical analyses were performed on samples collected along depth profiles. The analyses showed that the lakes harbored multiple and diverse prokaryotic communities whose distribution mirrored the water stratification patterns. Ursu Lake was found to be dominated by Bacteria and to have a greater prokaryotic diversity than Fara Fund Lake that harbored an increased cell density and was populated mostly by Archaea within oxic strata. In spite of their contrasting diversity, the microbial populations indigenous to each lake pointed to similar physiological functions within carbon degradation and sulfate reduction. Furthermore, the taxonomy results coupled with methane detection and its stable C isotope composition indicated the presence of a yet-undescribed methanogenic group in the lakes' hypersaline monimolimnion. In addition, ultrasmall uncultivated archaeal lineages were detected in the chemocline of Fara Fund Lake, where the recently proposed Nanohaloarchaeota phylum was found to thrive.
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Simulated Patient Data
<p>Generated with https://github.com/greenelab/DAPS/</p>
Three-layer exchange flow patterns driven by differential stratification in a micro-tidal fjord
<p>The datasets show transects at the mouth and along the Swedish Gullmar Fjord. They are taken from a realistic 3D model and were used to investigate three-layer exchange flows at the sill of the fjord.</p>
A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture (IMC data)
<p>This repository contains all <strong>raw imaging mass cytometry (IMC) data</strong> for the breast cancer study from Meyer et al., 2025. The code that was used to process and analyze the data is available at <a href="https://github.com/BodenmillerGroup/TNBC_publication">https://github.com/BodenmillerGroup/TNBC_publication</a>. </p> <p><strong>Structure:</strong><br>ZTMA174_raw.zip - Contains raw IMC data for ZTMA 174.</p> <p>ZTMA249_raw.zip - Contains raw IMC data for ZTMA 249.</p>
Illuminating Tycho's Rays: Automated Crater Census Uncovers Equilibrium Dynamics and Regolith Stratification on the Lunar Surface
<p>Excel files containing all crater data are output from ArcMap.</p> <p><br>DATA_HUMA_craters_raw contains two files for the *186 and *808 NAC images as coordinates and diameter (km). These are all the manual labelling for the NACs, with not-included data. Full set.</p> <p>DATA_YOLO_craters_raw contains several files for each sub-area, coordinates, diameter, and additional information</p> <p>DATA_selected_craters is a table that formed the <strong>basis for the analysis</strong>. It lists the crater sizes for each area (summed) and gives the surface area of the regions under investigation.</p>
Data from: New drivers of the evolution of mimetic accuracy in Batesian mimics: body size, habitat stratification and geographic zone affect accuracy of myrmecomorphic spiders
<p class="MsoBodyText">Aim: The evolution and maintenance of accurate Batesian mimicry has been explained by several hypotheses built upon relaxed selection. Such selection can be influenced by ecological factors, such as habitat type or geographic distribution, which have not been considered. I investigated whether the mimetic accuracy is influenced by habitat stratification where mimics occur (ground, low vegetation, bush, tree), their body size, and geographic distribution (temperate, subtropical, tropical).</p> <p class="MsoBodyText">Location: Worldwide</p> <p class="MsoBodyText">Taxon: Araneae</p> <p class="MsoBodyText">Methods: I gathered data on body size, geographic area of distribution, and habitat stratification from literature on more than 400 ant-mimicking (myrmecomorphic) spider species from 18 spider families and ranked them into four accuracy levels based on morphology, from poor inaccurate mimics to very accurate ones. Then I used regression to study the effect of body size, distribution, and habitat on mimetic accuracy while controlling for phylogeny.</p> <p class="MsoBodyText">Results: Mimetic accuracy increased with spider body size but differently at four types of habitat strata. On the ground and in low vegetation majority of smaller species were inaccurate, whereas on shrubs and trees even smaller species were accurate. The accuracy increased from temperate to the tropics but differently at the four habitat strata. In the temperate zone only species occurring on bushes were accurate, but in the tropical zone even ground-living species were accurate.</p> <p>Main conclusions: Higher accuracy at lower latitudes is likely due to stronger predation pressure from visually-hunting predators. Similarly, lower accuracy in species occurring near to the ground is presumably due to predation pressure by non-visually hunting predators. Inaccurate myrmecomorphy in spiders appears to be further driven by smaller body size due to lower profitability to predators; and higher latitude due to increased occurrence of generalist predators.</p>
Replication data for: A global perspective on social stratification in science
<p><strong>Replication materials for "A global perspective on social stratification in science"</strong></p> <p>This data comprises the number of Scopus authors by OECD macro field of science, country, authors' inferred gender, author's academic age, and bibliometric class. The sum of the column counts is: 8,225,368. </p> <p>**Please note**: there are 554 rows in the table indicated with "NA" where some of these variables are missing.</p> <p><strong>Paper metadata</strong><br>**Title**: A global perspective on social stratification in science</p> <p>**Authors**: Aliakbar Akbaritabara (Max Planck Institute for Demographic Research, Rostock, Germany), Andres F. Castro Torres (Max Planck Institute for Demographic Research, Rostock, Germany; Center for Demographic Studies, Barcelona, Spain), Vincent Larivière (Université de Montréal, Montréal, Canada)</p> <p>**Corresponding author and email**: Aliakbar Akbaritabar, akbaritabar@demogr.mpg.de</p> <p>**Abstract**: <br>To study stratification among scientists, we reconstruct the career-long trajectories of 8.2 million scientists worldwide using 12 bibliometric measures of productivity, geographical mobility, collaboration, and research impact. While most previous studies examined these variables in isolation, we study their relationships using Multiple Correspondence and Cluster Analysis. We group authors according to their bibliometric performance and academic age across six macro fields of science, and analyze co-authorship networks and detect collaboration communities of different sizes. We found a stratified structure in terms of academic age and bibliometric classes, with a small top class and large middle and bottom classes in all collaboration communities. Results are robust to community detection algorithms used and do not depend on authors’ gender. These results imply that increased productivity, impact, and collaboration are driven by a relatively small group that accounts for a large share of academic outputs, i.e., the top class. Mobility indicators are the only exception with bottom classes contributing similar or larger shares. We also show that those at the top succeed by collaborating with various authors from other classes and age groups. Nevertheless, they are benefiting disproportionately from these collaborations which may have implications for persisting stratification in academia.</p> <p>**Keywords**:<br>bibliometric data; research productivity; scientific collaboration; scientific mobility; scholarly impact and citations<br><br></p> <p><strong>Description of the data</strong><br>The dataset contains SIX columns as follows:</p> <p>**base_oecd**: Six OECD macro fields of science such as "Agricultural_Sciences", "Engineering_and_Technology", "Humanities", "Medical_and_Health_Sciences", "Natural_Sciences", "Social_Sciences", and "NA" (for missing) </p> <p>**base_countr**: Country name (see details in the paper)</p> <p>**gender**: Inferred gender based on the name of the author; it includes "fem" for "female", "mal" for "male", and "unkn" for "uknown" gender who are the authors whose gender could not be determined. Please see methods section for the details.</p> <p>**basc_acadag**: Academic age grouped into age groups such as "10-14", "15-20", "2-5", "21-25", "6-9", "One", and "NA" (for missing age group)</p> <p>**cluster6**: Bibliometric class assigned to authors aggregated at the country level as reported in the paper such as "BT", "ML", "LW", "MH", "TP", "HG", and "NA" (for missing). Please see methods section in the paper for the details.</p> <p>**count**: Number of unique authors</p> <p>For queries, contact: akbaritabar@demogr.mpg.de</p>
Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)
<p>Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)</p>
A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture (IF/mIF data)
<p>This repository contains all <strong>raw whole-slide immunofluorescence (IF) data</strong> for the breast cancer study from Meyer et al., 2025. The code that was used to process and analyze the data is available at <a href="https://github.com/BodenmillerGroup/TNBC_publication">https://github.com/BodenmillerGroup/TNBC_publication</a>. </p> <p><strong>Structure:</strong><br>BasoLum.zip - Contains whole-slide IF data for CK5, CK7, CK19 stainings of selected TNBC patients (related to Figure 4)</p> <p><strong>NOTE:</strong> Raw <strong>multiplexed whole-slide immunofluorescence (mIF) data</strong> (related to Figure 5) for this study is available from the corresponding author upon reasonable request and has not been uploaded to Zenodo due to the large data size (~ 50 GB per image). </p>
Dataset for "Moving towards genome-wide data integration for patient stratification with Integrate Any Omics"
Open the record for dataset details and reuse information.
Online Tables for the thesis "Clustering approaches for patient stratification in psychiatry" by Jonas Hagenberg
<p>This repository contains the online tables that accompany my thesis "Clustering approaches for patient stratification in psychiatry".</p> <p>In the following, I list the description of all tables:</p> <p> </p> <div> <div> <p>1: Information about somatic diseases and medication separated by status (participants labeled as cases and used in the clustering as well as controls without a DSM-IV diagnosis). N data denotes the number of individuals who had the information available. The medication information was not available for the OPTIMA cohort. N denotes the individuals who were affected by the disease or took the medication.</p> <p>2: Missingness and coefficient of variation information of the immune marker data for 237 participants used in the clustering and 36 controls without a DSM-IV diagnosis. The coefficient of variation was calculated from three internal controls that were run in duplicates.</p> <p>3: Variable importance of all variables the initial clustering. The variable importance is calculated as the F-value from an ANOVA model with the clusters as independent variables. The variable importance cannot be interpreted as a p-value as the variables were already used in the clustering, thereby inflating the p-values. Full version of supplementary table 3.</p> <p>4: Variable importance of all gene sets in the initial clustering. Full version of supplementary table 8.</p> <p>5: Variable importance of all variables the secondary analysis corrected for age, sex and BMI. Full version of supplementary table 9.</p> <p>6: Variable importance of all variables the exploratory analysis including cell type proportions. Full version of supplementary table 10.</p> <p>7: Variable importance of all gene sets in the exploratory analysis including cell type proportions. Full version of supplementary table 11.</p> <p>8: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 12.</p> <p>9: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 13.</p> <p>10: Enriched hallmark gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>11: Enriched hallmark gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>12: Enriched hallmark gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>13: Enriched GO biological pathway gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. Full version of supplementary table 15.</p> <p>14: Enriched GO biological pathway gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>15: Enriched GO biological pathway gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>16: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6.</p> <p>17: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6.</p> <p>18: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI.</p> <p>19: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI.</p> <p>20: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI.</p> <p>21: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI.</p> <p>22: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only CRP.</p> <p>23: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only IL-6.</p> <p>24: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained only BMI.</p> <p>25: Variable importance of initial multi-omics clustering of the DEGs identified in the single cell data set.</p> </div> </div>
Data from: Performance and agreement of risk stratification instruments for postoperative delirium in persons aged 50 years or older
Several risk stratification instruments for postoperative delirium in older people have been developed because early interventions may prevent delirium. We investigated the performance and agreement of nine commonly used risk stratification instruments in an independent validation cohort of consecutive elective and emergency surgical patients aged ≥50 years with ≥1 risk factor for postoperative delirium. Data was collected prospectively. Delirium was diagnosed according to DSM-IV-TR criteria. The observed incidence of postoperative delirium was calculated per risk score per risk stratification instrument. In addition, the risk stratification instruments were compared in terms of area under the receiver operating characteristic (ROC) curve (AUC), and positive and negative predictive value. Finally, the positive agreement between the risk stratification instruments was calculated. When data required for an exact implementation of the original risk stratification instruments was not available, we used alternative data that was comparable. The study population included 292 patients: 60% men; mean age (SD), 66 (8) years; 90% elective surgery. The incidence of postoperative delirium was 9%. The maximum observed incidence per risk score was 50% (95%CI, 15–85%); for eight risk stratification instruments, the maximum observed incidence per risk score was ≤25%. The AUC (95%CI) for the risk stratification instruments varied between 0.50 (0.36–0.64) and 0.66 (0.48–0.83). No AUC was statistically significant from 0.50 (p≥0.11). Positive predictive values of the risk stratification instruments varied between 0–25%, negative predictive values between 89–95%. Positive agreement varied between 0–66%. No risk stratification instrument showed clearly superior performance. In conclusion, in this independent validation cohort, the performance and agreement of commonly used risk stratification instruments for postoperative delirium was poor. Although some caution is needed because the risk stratification instruments were not implemented exactly as described in the original studies, we think that their usefulness in clinical practice can be questioned.
Data from: Modeling of the larval response of green sea urchins to thermal stratification using a random walk approach
Larval transport in the ocean can be affected by their vertical position in the water column. In biophysical models that are often used to predict larval horizontal dispersal, generally larval vertical positions are either ignored or incorporated as static parameters. Here, we evaluate the ability of one dimensional random walk based model to predict larval vertical distribution of Strongylocentrotus droebachiensis in response to thermal stratification. Vertical swimming velocities were recorded at various temperatures and used to parameterize the model. Data from a previous laboratory study on the effects of thermal stratification on larval vertical distribution of S. droebachiensis were compared to the model results to evaluate the predictive ability of the model. The model predicts general trends in vertical distribution fairly well, but has a systematic bias which can be explained by un-quantified larval behaviors at the boundaries of the experimental water column. Overall, our behavioral model successfully reproduces the mechanism which regulates larval vertical distribution in response to thermal structure. Collectively, the findings suggest that simple behavioral models parameterized using simple lab experiments can prove useful in estimating the vertical distributions of invertebrate larvae in the laboratory and likely in the ocean. Such models can then be linked to bio-physical models to more accurately predict larval dispersal.
Holocene Arabian Sea stratification and deoxygenation driven by weakened monsoon
<p>This file contains the carbon and oxygen isotopes of G. ruber (w) and Mg/Ca-derived SST, mixed layer species, and δ18O (‰, VPDB) IFA on G. ruber, T. sacculifer, and N. dutertrei from site SK240/496 in the northeastern Arabian Sea. In addition, SST (°C) stack, PC1 score based on proxy and reanalysis SST (°C), and PC1 score (Osman et al., 2021) and dysoxic species (z-score) from the Arabian Sea. </p>
Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer
<p>We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. In addition, 21 matched adjacent liver samples were also collected. All samples were subjected to RNA sequencing (RNA-seq). RNA sequencing was performed on Illumina NovaSeq 6000 platform with 40M pair-end 150bp reads per sample. Whole-exome sequencing (WES) was performed on 36 samples from patients T10, T13 and T18 (n = 26) as well as adjacent non-tumor tissues (n = 10). Paired end, 150bp read-length sequencing was then performed on Illumina NovaSeq 6000 platform with a mean sequencing coverage of 100X. Raw sequencing data has been deposited at the National Omics Data Encyclopedia (NODE) under the accession code OEP002956 ( <a href="http://www.biosino.org/node/project/detail/OEP002956">http://www.biosino.org/node/project/detail/OEP002956</a>).</p>
Data for dissertation titled 'Topic modelling for the stratification of neurological patients'
<p>The uploaded zip-file entails the data needed for and obtained through the dissertation titled 'Topic modelling for the stratification of neurological patients' as part of the programme 'MSc. in Statistical Data Analysis' at Ghent University. The study aimed at exploring the applicability of hierarchical stochastic block models on resting-state functional magnetic resonance imaging (RS-fMRI) to cluster participants with known neurological disorders.</p> <p>The data is structured in different folders and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (https://github.com/wvechelp/hsbm_on_fmri). The GitHub-repository already covers some example data, while additional data and results can be found in this zip-file. Additional comments on the analyses are also provided in the analysis scripts.</p> <p>The original raw data is obtained through the OpenFMRI project (<a href="http://openfmri.org/">http://openfmri.org/</a>, with label <em>ds000030</em>) and as a Stanford Digital Repository (<a href="https://purl.stanford.edu/mg599hw5271">https://purl.stanford.edu/mg599hw5271</a>) (Bilder<em> et al.</em>, 2016). It is obtained from the NIH Roadmap Initiative, as a result of the Consortium for Neuropsychiatric Phenomics (CNP) study (Poldrack<em> et al.</em>, 2016). Throughout the study, data was collected through interviews and rating scales, self-report measures, neurocognitive exams (using both paper-pencil and computerised tests), and a variety of neuroimaging data. More specific information on the selection procedure of the participants can be found in the description of the data (Bilder<em> et al.</em>, 2016) and the associated article (Poldrack<em> et al.</em>, 2016). Among the available neuroimaging data, the RS-fMRI data have been collected by asking participants to remain relaxed, while keeping their eyes open (with scans lasting 304 s and an image being collected every 2 seconds (Poldrack<em> et al.</em>, 2016)). This raw data was pre-processed by Rasero<em> et al.</em> (2019) to correct for motion and temporal alignment. Smoothing (6-mm full width at half-maximum Gaussian kernel), intensity normalisation, and a band-pass filter (between 0.01 and 0.08 Hz) were applied prior to the removal of linear and quadratic trends. Motion time courses, average CSF signal, and the average white matter signal were regressed out prior to data transformation into voxels with a volume of 3 mm x 3 mm x 3 mm. Ultimately, the functional atlas of Shen<em> et al.</em> (2013) was used to average the voxel signals per anatomical region of interest (ROI), resulting in a parcellation of 278 ROIs (and associated time series consisting of 152 measurements). From these ROI-specific time series, Rasero<em> et al.</em> (2019) generated 278 x 278 matrices with Pearson coefficients.</p> <p>References:<br> Bilder, R. M., Poldrack, R. A., Cannon, T., London, E., Freimer, N., Congdon, E., Karlsgodt, K., & Sabb, F. W. (2016). <em>UCLA Consortium for Neuropsychiatric Phenomics LA5c Study</em> Stanford Digital Repository. <a href="http://purl.stanford.edu/mg599hw5271">http://purl.stanford.edu/mg599hw5271</a> and <a href="https://openfmri.org/dataset/ds000030/">https://openfmri.org/dataset/ds000030/</a><br> Poldrack, R. A., Congdon, E., Triplett, W., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., & Bilder, R. M. (2016). A phenome-wide examination of neural and cognitive function. <em>Scientific Data</em>,<em> 3</em>(1), 160110. <a href="https://doi.org/10.1038/sdata.2016.110">https://doi.org/10.1038/sdata.2016.110</a><br> Rasero, J., Diez, I., Cortes, J. M., Marinazzo, D., & Stramaglia, S. A.-O. (2019). Connectome sorting by consensus clustering increases separability in group neuroimaging studies. <em>Network Neuroscience</em>,<em> 3</em>(2), 325-343. <a href="https://doi.org/https:/doi.org/10.1162/netn_a_00074">https://doi.org/https://doi.org/10.1162/netn_a_00074</a><br> Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. <em>NeuroImage</em>,<em> 82</em>, 403-415. <a href="https://doi.org/https:/doi.org/10.1016/j.neuroimage.2013.05.081">https://doi.org/https://doi.org/10.1016/j.neuroimage.2013.05.081</a></p> <p> </p>
Risk Stratification of Rapid Disease Progression in Children With Crohn's Disease
ClinicalTrials.gov study NCT00790543. IPD Sharing: NO. Countries: 2. Publications: 1.
Cardiac Magnetic Resonance for Risk Stratification in Non-dilated Left Ventricular Cardiomyopathy
ClinicalTrials.gov study NCT07351838. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
ScienceDex guides
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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.