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 “empirical strategy”
Empirical Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<h3>Description of the data and file structure</h3> <p>The files contain the source data for Fig. 1 and Fig. 3A,B of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <h4>Files and variables</h4> <h5>File: Fountain_Fish_coordinates.zip</h5> <p><strong>Description:</strong> </p> <p>The zip file contains 30 folders, each containing information on one predator attack and respective prey evasion. Each folder is named according to the drone ID used for the filming (e.g., DJI _1, DJI _2, DJI _3) and the respective frame number (e.g., f930) from the video recording. </p> <h5>File naming</h5> <p>Each folder contains JPG and CSV files. </p> <p>The JPG files show the image from the footage at the corresponding frame indicated in the files' name (e.g., frame_001_im).</p> <p>There are 2 types of CSV files. Files with the name 'polygon.csv' contain coordinates (in pixels) of points (x, y) defining the polygon outlining the prey school at the particular frame as indicated in the files' name (e.g., frame001) and corresponding to the image in the JPG file with the same frame number. Files with the name 'sardines_and_marlin.csv' contain coordinates (in pixels) of points (x, y), defining the head (columns 1 and 2) and the dorsal fin (columns 3 and 4) of single sardine individuals (by rows), and of the respective attacking marlin: marlin's head (columns 5 and 6), marlin's dorsal fin (columns 7 and 8), and marlin's tip of the bill (columns 9 and 10). These coordinates correspond to the respective image with the same frame number.</p>
Temporal validity of software datasets for code metrics: an empirical assessment of sampling strategies
<p>This is the repository for the scripts and data of the study "Building and updating software datasets: an empirical assessment".</p> <h2>Data collected</h2> <p>The data generated for the study it can be downloaded as a zip file. Each folder inside the file corresponds to one of the datasets of projects employed in the study (qualitas, currentSample and qualitasUpdated). Every dataset comprised three files "class.csv", "method.csv" and "sample.csv", with class metrics, method metrics and repository metadata of the projects respectively. Here is a description of the datasets:</p> <ul> <li>qualitas: includes code metrics and repository metrics from the projects in the release 20130901r of the Qualitas Corpus.</li> <li>currentSample: includes code metrics and repository metrics from a recent sample collected with our sampling procedure.</li> <li>qualitasUpdated: includes code metrics and repository metrics from an updated version of the Qualitas Corpus applying our maintenance procedure.</li> </ul> <h2>Plot graphics</h2> <p>To plot the results and graphics in the article there is a Jupyter Notebook "Experiment.ipynb". It is initially configured to use the data in "datasets" folder.</p> <h2>Replication Kit</h2> <p>For replication purposes, the datasets containing recent projects from Github can be re-generated. To do so, the virtual environment must have installed the dependencies in "requirements.txt" file, add Github's tokens in "./token" file, re-define or leave as is the paths declared in the constants (variables written in caps) in the main method, and finally run "main.py" script. The portable versions of the source code scanner <a href="https://sourcemeter.com/" target="_blank" rel="noopener">Sourcemeter</a> are located as zip files in "./Sourcemeter/tool" directory. To install Sourcemeter the appropriate zip file must be decompressed excluding the root folder "SourceMeter-10.2.0-x64-<OS>".</p> <p>The script comprise 5 steps:</p> <ol> <li>Project retrieval from Github: at first the sampling frame with projects complying with a specific quality criteria are retrieved from Github's API.</li> <li>Create samples: with the sampling frame retrieved, the current samples are selected (currentSample and qualitasUpdated). In the case of qualitasUpdated, it is important to have first the "sample.csv" file inside the qualitas folder of the dataset originally created for the study. This file contains the metadata of the projects in Qualitas Corpus.</li> <li>Project download and analysis: when all the samples are selected from the sampling frame (currentSample and qualitasUpdated), the repositories are downloaded and scanned with SourceMeter. In the cases in which the analysis is not possible, the projects are replaced with another one with similar size.</li> <li>Outlier detection: once the datasets are collected, it is necessary to manually look for possible outliers in the code metrics under study. In the notebook "Experiment.ipynb" there are specific sections dedicated for it ("Outlier detection (Section 4.2.2)").</li> <li>Outlier replacement: when the outliers are detected, in the same notebook there is also a section for outlier replacement ("Replace Outliers") where the outliers' url have to be listed to find the appropriate replacement.</li> </ol> <ul> <li>If it is required, the metrics from the Qualitas Corpus can also be re-generated. First, it is necessary to download the release 20130901r from its <a href="http://www.qualitascorpus.com/download/" target="_blank" rel="noopener">official webpage</a>. Second, decompress the .tar files downloaded. Third, make sure that the compressed files with source code from the projects (.java files) are placed in the "compressed" folder, in some cases it is necessary to read the "QC_README" file in the project's folder. Finally, run the original main script "Generate metrics for the Qualitas Corpus (QC) dataset" part of the code. </li> </ul>
Comparison of 2 Antifungal Treatment (Empirical Versus Pre-Empirical) Strategies in Prolonged Neutropenia
ClinicalTrials.gov study NCT00190463. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the Latent Environmental Threshold Model
Conditional strategies are the most common form of discrete phenotypic plasticity. In a conditional strategy, the phenotype expressed by an organism is determined by the difference between an environmental cue and a threshold, both of which may vary among individuals. The Environmental Threshold model (ETM) has been proposed as a mean to understand the evolution of conditional strategies, but has been surprisingly seldom applied to empirical studies. A hindrance for the application of the ETM is that often, the proximate cue triggering the phenotypic expression and the individual threshold are not measurable, and can only be assessed using a related observable cue. We describe a new statistical model that can be applied in this common situation. The Latent Environmental Threshold Model (LETM) allows for a measurement error in the phenotypic expression of the individual environmental cue and a purely genetically determined threshold. We show that coupling our model with quantitative genetic methods allows an evolutionary approach including an estimation of the heritability of conditional strategies. We evaluate the performance of the LETM with a simulation study and illustrate its utility by applying it to empirical data on the size-dependent smolting process for stream-dwelling Atlantic salmon juveniles.
Trial Comparing a Strategy Based on Molecular Analysis to the Empiric Strategy in Patients With CUP
ClinicalTrials.gov study NCT01540058. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Empiric Versus Selective Prevention Strategies for Kidney Stone Disease
ClinicalTrials.gov study NCT05365477. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the Latent Environmental Threshold Model
Open the record for dataset details and reuse information.
Empirical Data, Survey and Letter of Consent of the Study: Brouillet C. et al. "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services"
<ul> <li>Empirical Data (quantitative part of the results), Survey and Letter of Consent</li> <li>From the study entitled "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services" Brouillet C. et al. </li> <li>All documents are in French.</li> </ul>
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.