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.
37
datasets available to search
ShareScore release 0.9.0
Dataset results
37 results for “Empirical comparison”
Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations
<p>Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values’ range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R>1 (excess deaths exceeding COVID-19 deaths) while others suggest R<1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated. </p>
An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization
<p>This is the data and source code used in the paper below:</p> <p>Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck, “An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi: 10.1109/SSCI44817.2019.9002805</p> <p>This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for robust optimization. The experimental setup includes three noise levels, six meta-modeling algorithms, and six benchmark problems from the continuous optimization domain, each for three different dimensionalities. Two robustness definitions: robust regularization and robust composition, are used in the experiments. The meta-modeling techniques are evaluated and compared with respect to the modeling accuracy and the optimal function values. The results clearly show that Kriging, Support Vector Machine and Polynomial regression perform excellently as they achieve high accuracy and the optimal point on the model landscape is close to the true optimum of test functions in most cases.</p>
An Empirical Comparison of Pre-Trained Models of Source Code
<p>The replication package of the paper "An Empirical Comparison of Pre-Trained Models of Source Code". For the source code, please refer to <a href="https://github.com/NougatCA/FineTuner">https://github.com/NougatCA/FineTuner</a>.</p>
Replication Package for "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems"
<p>This is the replication package for the article "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems" published in the Empirical Software Engineering journal.</p> <p>This package requires Python 3.5 and all the dependencies that are listed in "requirements.txt".<br> The notebooks (in "notebooks" folder) should be opened and executed with Jupyter.</p> <p>The notebooks require the graphs (in "graphs" folder) to be computed first. To do so, execute "helpers.py" with Python.<br> The graphs are built using the data provided by https://libraries.io under CC BY-SA<br> https://creativecommons.org/licenses/by-sa/4.0/<br> Those data can be found in the "data" folder.</p> <p> </p>
Protein quantification in ecological studies: a literature review and empirical comparisons of standard methodologies
Open the record for dataset details and reuse information.
Inferring competitive outcomes, ranks and intransitivity from empirical data: A comparison of different methods
Open the record for dataset details and reuse information.
Lost in Zero Space -- An Empirical Comparison of 0.y.z Releases in Four Software Package Distributions
<pre>The notebooks in "notebooks/" require the dependencies specified in "requirements.txt" to be installed. They rely on data files in "data/". These data can be obtained by running the "convert.py" scripts in that folder. The script requires data files from "data-raw/". These files can be generated by running "extract.py" in that folder. This script requires the libraries.io data dump, as explained in the README file contained in this folder. If you don't have these data, or if you don't want to download them, you can ask for the required data/*.csv.gz files by email.</pre>
Data from: An empirical comparison of SNPs and microsatellites for parentage and kinship assignment in a wild sockeye salmon (Oncorhynchus nerka) population
Because of their high variability, microsatellites are still considered the marker of choice for studies on parentage and kinship in wild populations. Nevertheless, single nucleotide polymorphisms (SNPs) are becoming increasing popular in many areas of molecular ecology, owing to their high-throughput, easy transferability between laboratories and low genotyping error. An ongoing discussion concerns the relative power of SNPs compared to microsatellites – that is, how many SNP loci are needed to replace a panel of microsatellites? Here, we evaluate the assignment power of 80 SNPs (HE=0.30, 80 independent alleles) and 11 microsatellites (HE =0.85, 194 independent alleles) in a wild population of about 400 sockeye salmon with two commonly used software packages (Cervus3, Colony2) and, for SNPs only, a newly developed software (SNPPIT). Assignment success was higher for SNPs than for microsatellites, especially for parent pairs, irrespective of the method used. Colony2 assigned a larger proportion of offspring to at least one parent than the other methods, though Cervus and SNPPIT detected more parent pairs. Identification of full sib groups without parental information from relatedness measures was possible using both marker systems, though explicit reconstruction of such groups in Colony2 was impossible for SNPs because of computation time. Our results confirm the applicability of SNPs for parentage analyses and refute the predictability of assignment success from the number of independent alleles.
Data from: Applicability of RAD-tag genotyping for inter-familial comparisons: empirical data from two cetaceans
Restriction site-Associated DNA tag (RAD-tag) sequencing has become a popular approach to generate thousands of SNPs used to address diverse questions in population genomics. Comparatively, the suitability of RAD-tag genotyping to address evolutionary questions across divergent species has been the subject of only a few recent studies. Here, we evaluate the applicability of this approach to conduct genome-wide scans for polymorphisms across two cetacean species belonging to distinct families: the short-beaked common dolphin (Delphinus delphis; n = 5 individuals) and the harbor porpoise (Phocoena phocoena; n = 1 individual). Additionally, we explore the effects of varying two parameters in the Stacks analysis pipeline on the number of loci and level of divergence obtained. We observed a 34% drop in the total number of loci that were present in all individuals when analyzing individuals from the distinct families compared to analyses restricted to intra-specific comparisons (i.e., within D. delphis). Despite relatively stringent quality filters, 3,595 polymorphic loci were retrieved from our inter-familial comparison. Cetaceans have undergone rapid diversification and the estimated divergence time between the two families is relatively recent (14 to 19 My). Thus, our results showed that, for this level of divergence, a large number of orthologous loci can still be genotyped using this approach, which is on par with two recent in silico studies. Our findings constitute one of the first empirical investigations using RAD-tag sequencing at this level of divergence and highlights the great potential of this approach in comparative studies and to address evolutionary questions.
Anatomy of top 1% most highly-cited publications. An empirical comparison of two approaches. Dataset
<p>Supplementary material containing tables with the main pieces of data used in the publication entitled Anatomy of top 1% most highly-cited publications. An empirical comparison of two approaches. This pieces of data were downloaded from the November 2022 snapshot of OpenAlex.</p>
Master Thesis- Modeling of Electric Vehicle Charging Infrastructure and Comparison of Electric Vehicle Load Simulation with Empirical Charging Data
<p>All the data behind relevant plots in the thesis report are stored here</p>
Data from: The prediction of adaptive evolution: empirical application of the secondary theorem of selection and comparison to the breeder's equation
Adaptive evolution occurs when fitness covaries with genetic merit for a trait (or traits). The breeder's equation (BE), in both its univariate and multivariate forms, allows us to predict this process by combining estimates of selection on phenotype with estimates of genetic (co)variation. However, predictions are only valid if all factors causal for trait-fitness covariance are measured. While this requirement will rarely (if ever) be met in practice, it can be avoided by applying Robertson's secondary theorem of selection (STS). The STS predicts evolution by directly estimating the genetic basis of trait-fitness covariation with out any explicit model of selection. Here we apply the BE and STS to four morphological traits measured in Soay sheep (Ovis aries) from St. Kilda. Despite apparently positive selection on heritable size traits, sheep are not getting larger. However, while the BE predicts increasing size the STS does not, a discrepancy that suggests unmeasured factors are upwardly biasing our estimates of selection on phenotype. We suggest this is likely to be a general issue, and that wider application of the STS could offer at least a partial resolution to the common discrepancy between naive expectations and observed trait dynamics in natural populations.
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree STEM</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gender</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gap</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree rights</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Empirical conceptual map</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Theoretical concept map</p>
Do Agile Scaling Approaches Make A Difference? An Empirical Comparison of Team Effectiveness Across Popular Scaling Approaches
<p>This bundle contains supplementary materials for an upcoming academic publication <em>Do Agile Scaling Approaches Make A Difference? An Empirical Comparison of Team Effectiveness Across Popular Scaling Approaches?</em>, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset and SPSS syntaxes. This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).</p> <p><strong>About the dataset</strong></p> <p>The dataset (SPSS) contains anonymized response data from 15,078 team members aggregated into 4,013 Agile teams that participated from <a href="https://scrumteamsurvey.org">scrumteamsurvey.org</a>. Stakeholder evaluations of 1,841 stakeholders were also collected for 529 of those teams. Data was gathered between September 2021, and September 2023. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-50%). No data was missing at the team level.</p> <p>Question labels and option labels are provided separately in Questions.csv. To conform to the privacy statement of <a href="https://scrumteamsurvey.org">scrumteamsurvey.org</a>, the bundle does not include response data from before the team-level aggregation.</p> <p><strong>About the SPSS syntaxes</strong></p> <p>The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.</p>
Comparison Between Effect of Empirical Antibiotic Prophylaxis Versus Enhanced Prophylactic Measures on Rate of Urinary Tract Infection After Flexible Ureteroscopy in Children With Pyuria
ClinicalTrials.gov study NCT07229755. IPD Sharing: YES. Countries: 1. Publications: 1.
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.