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.
61
datasets available to search
ShareScore release 0.9.0
Dataset results
61 results for “sampling strategy”
Analysis of CA-MRSA Transmission: An ED Population Sampling Strategy
ClinicalTrials.gov study NCT02363166. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Sequential Combined Versus Single-Strategy Adrenal Venous Sampling for Primary Aldosteronism(SCOPE)
ClinicalTrials.gov study NCT07298954. IPD Sharing: NO. Countries: 1. Publications: 25.
Network-Targeted Strategies for Efficient Community SARS-CoV-2 (COVID-19) Sampling
ClinicalTrials.gov study NCT04437706. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: More for less: sampling strategies of plant functional traits across local environmental gradients
Open the record for dataset details and reuse information.
Data from: Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach
Open the record for dataset details and reuse information.
Experimental Data Set for the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy"
<p>This are the feature values used in the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy".</p> <p>The dataset regroups feature values for every "cheap" features available in the R package <em>flacco </em>and are computed using 5 sampling strategies and in dimension <span class="math-tex">\($d=5$\)</span>:</p> <ol> <li>Random: the classical Mersenne-Twister algorithm;</li> <li>Randu: a random number generator that is notoriously bad;</li> <li>LHS: a centered Latin Hypercube Design;</li> <li>iLHS: an improved Latin Hypercube Design;</li> <li>Sobol: points extracted from a Sobol' low-discrepancy sequence.</li> </ol> <p>The csv file <em>features_summury_dim_5_ppsn.csv </em>regroups 100 values for every features whereas <em>features_summury_dim_5_ppsn_median.csv </em>regroups for every feature the median of the 100 values.</p> <p>In the folder <em>PPSN_feature_plots</em> are the histograms of feature values on the 24 COCO functions for 3 sampling strategies: Random, LHS and Sobol.</p> <p>The Python file <em>sampling_ppsn.py</em> is the code used to generate the sample points from which the feature values are computed.</p> <p>The file <em>stats50_knn_dt.csv</em> provide the raw data of median and IQR (inter quartile interval) for the heatmaps and boxplots available in the paper.</p> <p>Finally, the files <em>results_classif_knn100.csv</em> (resp. dt) provide the accuracy of 100 classifications for every settings.</p> <p> </p>
Data from: Sampling strategies for delimiting species: genes, individuals, and populations in the Liolaemus elongatus-kriegi complex (Squamata: Liolaemidae) in Andean-Patagonian South America
Recovery of evolutionary history and delimiting species boundaries in widely distributed, poorly-known groups requires extensive geographic sampling, but this is difficult to design a priori because evolutionary diversity is often "hidden" by an inadequate taxonomy. Large data sets are needed, and these provide unique challenges for analysis when they span intra and inter-specific levels of divergence. Protocols have been designed to combine methods of analysis for DNA sequences that exhibit both very shallow and relatively deeper divergences (Crandall and Fitzpatrick, 1996). In this study we combine several tree-based phylogeny reconstruction methods with nested clade analysis, to extract maximum historical signal at various levels, in the poorly-known Liolaemus elongatus-kriegi complex in temperate South America. We implement the basic protocol of Wiens and Penkrot (2002) to test for species boundaries, and propose modifications to accommodate large data sets and gene regions with heterogeneous substitution rates. Combining haplotype trees with nested-clade analyses allowed testing of species boundaries on the basis of a priori defined criteria, and this approach suggests that the number of putative species could be doubled. We discuss these findings in the context of the advantages and limitations of a combined approach for retrieval of maximum historical information in large data sets, in the context of the yet formidable unresolved issues of sampling strategies.
Supplementary material 1 from: Bylemans J, Gleeson DM, Lintermans M, Hardy CM, Beitzel M, Gilligan DM, Furlan EM (2018) Monitoring riverine fish communities through eDNA metabarcoding: determining optimal sampling strategies along an altitudinal and biodiversity gradient. Metabarcoding and Metagenomics 2: e30457. https://doi.org/10.3897/mbmg.2.30457
: Data type: Microsoft Word Document (.docx)
Supplementary material 2 from: Bylemans J, Gleeson DM, Lintermans M, Hardy CM, Beitzel M, Gilligan DM, Furlan EM (2018) Monitoring riverine fish communities through eDNA metabarcoding: determining optimal sampling strategies along an altitudinal and biodiversity gradient. Metabarcoding and Metagenomics 2: e30457. https://doi.org/10.3897/mbmg.2.30457
: Data type: statistical data
Exploring Protein Conformational Changes Using a Large-scale Biophysical Sampling Augmented Deep Learning Strategy
Open the record for dataset details and reuse information.
PP-recyclate characterization after different sampling and recycling strategies-HPLC-MS
<p>The purpose of this analysis is to evaluate the efficiency of different recycling procedures and the quality of the resulting recyclates.</p> <p>This dataset contains data of PP-recyclates from different recycling strategies. The content is:</p> <ul> <li>One Excel file containing LC-MS data of PP recyclates after scCO2 recycling, reference samples and measurement protocol</li> <li>One Excel file containing LC-MS data for DEHP quantification of PP recyclates, reference samples and measurement protocol</li> <li>One Readme file containing further information about the methodology and nomenclature</li> </ul> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p>
PP-recyclates characterization after different sampling and recycling strategies-Parallel Plate Rheology data
<p>The purpose of this analysis is to evaluate the efficiency of different recycling procedures and the quality of the resulting recyclates.</p> <p>This dataset contains raw parallel plate rheology data of PP-recyclates from different recycling strategies. The content is:</p> <ul> <li>One Excel file containing parallel plate rheology data of PP recyclates after scCO2 recycling, reference samples and measurement protocol</li> <li>One Excel file containing parallel plate rheology of PP recyclates after solvent-based recycling, reference samples and measurement protocol</li> <li>One Excel file containing parallel plate rheology data of PP recyclates after upcycling, reference samples and measurement protocol</li> <li>One Readme file containing further information about the methodology and nomenclature</li> </ul> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p>
Data from: Design- and model-based strategies for detecting and quantifying an amphibian pathogen in environmental samples
Open the record for dataset details and reuse information.
Data from: Sampling strategies for delimiting species: genes, individuals, and populations in the Liolaemus elongatus-kriegi complex (Squamata: Liolaemidae) in Andean-Patagonian South America
Open the record for dataset details and reuse information.
CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [HAP1]
GEO Series GSE153108. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing
GEO Series GSE153116. Homo sapiens; Canis lupus familiaris; Chlorocebus sabaeus; Drosophila melanogaster; Rattus norvegicus; Cricetulus griseus; Mus musculus. 197 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [snATAC]
GEO Series GSE153115. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [mixed Sample of cell lines and organisms]
GEO Series GSE153113. Homo sapiens; Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Data from: Diversity in Müllerian mimicry: the optimal predator sampling strategy explains both local and regional polymorphism in prey
The convergent evolution of warning signals in unpalatable species, known as Müllerian mimicry, has been observed in a wide variety of taxonomic groups. This form of mimicry is generally thought to have arisen as a consequence of local frequency-dependent selection imposed by sampling predators. However, despite clear evidence for local selection against rare warning signals, there appears an almost embarrassing amount of polymorphism in natural warning colors, both within and among populations. Because the model of predator cognition widely invoked to explain Müllerian mimicry (Müller's "fixed nk" model) is highly simplified and has not been empirically supported; here, we explore the dynamical consequences of the optimal strategy for sampling unfamiliar prey. This strategy, based on a classical exploration–exploitation trade-off, not only allows for a variable number of prey sampled, but also accounts for predator neophobia under some conditions. In contrast to Müller's "fixed nk" sampling rule, the optimal sampling strategy is capable of generating a variety of dynamical outcomes, including mimicry but also regional and local polymorphism. Moreover, the heterogeneity of predator behavior across space and time that a more nuanced foraging strategy allows, can even further facilitate the emergence of both local and regional polymorphism in prey warning color.
PP-recyclates characterization after different sampling and recycling strategies - ATR-FTIR data
<p>The purpose of this analysis is to evaluate the efficiency of different recycling procedures and the quality of the resulting recyclates.</p> <p>This dataset contains raw parallel plate rheology data of PP-recyclates from different recycling strategies. The content is:</p> <ul> <li>One Excel file containing ATR-FTIR data of PP recyclates after scCO2 recycling, reference samples and measurement protocol</li> <li>One Excel file containing ATR-FTIR of PP recyclates after solvent-based recycling, reference samples and measurement protocol</li> <li>One Excel file containing ATR-FTIR data of PP recyclates after upcycling, reference samples and measurement protocol</li> <li>One Readme file containing further information about the methodology and nomenclature</li> </ul> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</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.