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.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “combining methodologies”
Supporting materials for the methodology for optimal combinations of agroecological practices (AEPs)
<p><span>The assessment framework is developed by first reviewing existing agroecological sustainability assessment tools. Indicators are collected based on literature review. Then they are synthesized into the holistic agroecology assessment framework. New indicators are developed during the project and are also used to address any context-specific data needs or cover gaps of the existing tools. </span></p>
Methodology for optimal combinations of agroecological practices (AEPs)
<table> <tbody> <tr> <td>The assessment framework is developed by first reviewing existing agroecological sustainability assessment tools. Indicators are collected based on literature review. Then they are synthesized into the holistic agroecology assessment framework. New indicators are developed during the project and are also used to address any context-specific data needs or cover gaps of the existing tools.<span> </span></td> </tr> </tbody> </table>
Combined bioinformatics and machine learning methodologies reveal prognosis-related ceRNA network and propose ABCA8, CAT, and CXCL12 as independent protective factors against osteosarcoma
<p><strong>Supplementary Table 1</strong>. Basic traits of the seven microarray datasets from the Gene Expression Omnibus and The Cancer Genome Atlas.</p><p><strong>Supplementary Table 2</strong>. Basic characteristics of the nine differentially expressed circRNAs</p><p><strong>Supplementary Table 3</strong>. Index of concordance (C-index) and variance inflation factor (VIF) of ABCA8, CXCL12, and CAT.</p><p><strong>Supplementary Table 4.</strong> LASSO and cox analysis of ceRNA with coef/se(coef) < 0.01 and P-value of proportional hazards assumption (PH) > 0.05.</p><p><strong>Supplementary Table 5</strong>. Robust rank aggregation analysis of ABCA8, CXCL12, and CAT. LogFC in the four datasets and RRA score of the three RNAs.</p><p><strong>Supplementary Figure 1.</strong> Competitive endogenous RNA in osteosarcoma.</p><p><strong>Supplementary Figure 2.</strong> Protein–protein interaction (PPI) network of genes in the competitive endogenous RNA network</p><p><strong>Supplementary Figure 3.</strong> Proportional hazards assumption (left) and linearity assumption (right).</p><p><strong>Supplementary Figure 4.</strong> Survival analysis for competitive endogenous RNA in osteosarcoma.</p>
Supplementary files for "Building bridges between natural and social science disciplines: A standardised methodology to combine data on ecosystem quality trends"
<p>Data sources and mapping methodology</p>
Identification of crashworthy designs combining active learning and the solution space methodology - Validation and training dataset
<p>Dataset and Python codes for training a crashworthiness classifier.</p>
Structure determination of oleanolic and ursolic acids: a combined DFT/vibrational spectroscopy methodology
Open the record for dataset details and reuse information.
Combining biogeographic approaches to advance invasion ecology and methodology
<p>1) Understanding the causes of plant invasions requires that parallel field studies are conducted in the native and introduced ranges to elucidate how biogeographic shifts alter the individual performance, population success, and community-level impacts of invading plants. Three primary methods deployed in in situ biogeographic studies are directed surveys, where researchers seek out populations of target species, randomized surveys, and field experiments. Despite the importance of these approaches for advancing biogeographic research, their relative merits have not been evaluated.</p> <p>2) We concurrently deployed directed surveys, randomized surveys, and in situ field experiments for studying six grassland plant species in the native and introduced ranges. Metrics included plant size, fecundity, recruitment, abundance, and invader impact, as well as soil properties and root associations with putative fungal mutualists and pathogens.</p> <p>3) Consistent with key invasion hypotheses, Bromus tectorum experienced increased size and fecundity in the introduced range linked to population increases and significant invader impacts, along with altered fungal associations. However, performance differences did not predict population increases and invader impacts across species. Rather, the differential effect of disturbance in facilitating greater recruitment in the introduced range appeared to play a crucial, though previously underexplored, role in driving invader success.</p> <p>4) Directed surveys consistently generated information on plant performance and fungal associations. However, soil sampling suggested that directed surveys may have been biased toward disturbed conditions for half the species. Randomized surveys generated robust data for population comparisons and impact, but generally failed to produce performance metrics for species that were uncommon or flowered outside the peak sampling window. Field experiments controlled for bias and confounding factors and provided rare information on recruitment and disturbance effects, but scant recruitment in the native range and ethical constraints on growing invaders in the introduced range limited information on performance and plant-fungal interactions.</p> <p>5) Synthesis. Each method had strengths and weaknesses. However, when combined they provided complementary information to paint the most complete biogeographic picture to date for several introduced plants. We propose a hybrid approach to optimize biogeographic studies.</p>
Combining biogeographic approaches to advance invasion ecology and methodology
Open the record for dataset details and reuse information.
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.