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Simulated data for: Long-term persistence of exaggerated ornaments under Fisherian runaway despite costly mate search
<p><span>This repository contains simulated datasets relating to:</span></p> <p><span>Anna Waffender, Jonathan M. Henshaw</span></p> <p><span>"Long-term persistence of </span><span>exaggerated ornaments under Fisherian runaway despite costly mate search"</span></p> <p><span> </span></p> <p><span>We simulate the evolution of male ornaments and female preferences under Fisherian runaway. We include costly mate search effort, reducing female fecundity. </span></p> <p><span>Datasets are organized into CSV files named after the corresponding figure in the publication. For further information see the publication and the file README.md in this repository.</span></p>
Literature search on worldwide surveillance systems targeting transboundary zoonotic and emerging diseases
<p>Surveillance systems for zoonotic and transboundary emerging pathogens that are structured following the holistic principles of joint work efforts from the human health, animal health and environmental health sectors are reviewed to provide a summary of one-health based surveillance systems existing worldwide. A systematic search of available literature was undertaken across various biomedical and scientific literature databases (from 2000 to 2022) and were selected using inclusion/exclusion criteria to filter references presenting systematic surveillance systems applicable to transmissible, transboundary, and zoonotic diseases operating under the One Health approach. A standardized data model and vocabulary were used to extract and classify key information to characterize target surveillance systems. 996 studies were obtained after research (589 after duplicates’ elimination) for inclusion in this review and information was extracted using a data model, which were reduced to 79 items once inclusion and exclusion criteria were applied. From these articles, 80 additional items were found within references.</p>
Survey for ranking preferences in search for biological datasets
<p>We conducted a survey from November 2018 to March 2019 in Germany among 26 scholars with expertise in biodiversity research to determine preferences in dataset search.<br> In particular, we wanted to explore suitable semantic extensions in the search result and possible additional recommendations on related topics that scholars support in retrieving relevant datasets.</p> <p>The scholars were faced with four different search scenarios and pre-defined answer options. They gave their ratings on a 5-point-Likert scale.<br> The scenarios addressed different important topics in biodiversity research, e.g., a search for organisms, materials, processes and data parameters.</p> <p>This folder contains the original survey results as well as the analysis per topic (category).</p>
AI-SPRINT SPACE4AI-R Local Search
<p>This repository includes the source code and the evaluation data and results for the Local Search algorithm implemented for the SPACE4AI-R Optimizer framework. An extended discussion is reported in the AI-SPRINT project deliverable "D3.3 - Second release and evaluation of the runtime environment".</p>
ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search – Replication Package
<p>This is the replication package associated with the paper "<em>ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search</em>" accepted at the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023) – Technical Track. Cite this paper using the following:</p> <p><em>@inproceedings{pan2023atm,<br> title={ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search},<br> author={Pan, Rongqi and Ghaleb, Taher A. and Briand, Lionel},<br> booktitle={Proceedings of the 45th IEEE/ACM International Conference on Software Engineering},<br> year={2023},<br> pages={1--12}<br> }</em></p> <p><strong>Replication Package Contents:</strong><br> The replication package contains all the necessary data and code required to reproduce the results reported in the paper. We also provide the results for other minimization budgets, and detailed <em>FDR,</em> execution time, and statistical test results. In addition, we provide the data and code required to reproduce the results of baselines techniques: FAST-R and random minimization.</p> <p><strong>Data:</strong><br> We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is based on 16 projects from <a href="https://github.com/rjust/defects4j">Defects4J</a>, whose characteristics can be found in <em><strong>Data/subject_projects.csv</strong></em><em>.</em></p> <p><strong>Code:</strong><br> We provide in the <em><strong>Code</strong></em> directory the code and scripts (Java, Python, and Bash) required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong><br> We provide in the <em><strong>Results</strong></em> directory the results for each technique independently, and also a summary of all results together for comparison purposes. The source code for this step is in the <em><strong>Code/ATM/CodeToAST</strong></em> directory. The source code for this step is in the <em><strong>Code/ATM/Similarity</strong></em> directory.</p> <p><strong>_________________________________</strong></p> <p><strong>ATM - Code to AST transformation:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory)</p> <p><strong>Input:</strong><br> All zipped data files should be unzipped before running each step.<br> * Data/test_suites/all_test_cases.zip → Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases.zip → Data/test_suites/changed_test_cases<br> * Data/test_suites/relevant_test_cases.zip → Data/test_suites/relevant_test_cases</p> <p><strong>Output:</strong><br> * Data/ATM/ASTs/all_test_cases<br> * Data/ATM/ASTs/changed_test_cases</p> <p><strong>Running the experiment:</strong><br> To generate ASTS for all test cases in the project test suites, the <em><strong>Code/ATM/CodeToAST/src/CodeToAST.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/CodeToAST/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/CodeToAST bash transform_code_to_ast.sh</code></pre> <p>Each test file in the <em><strong>Data/test_suites/all_test_cases</strong></em> and <em><strong>Data/test_suites/changed_test_cases</strong></em> directories is parsed to generate a corresponding AST for each test case method (saved in an XML format in <strong>Data/ATM/ASTs/all_test_cases</strong> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> for each project version)<br> <strong>_________________________________</strong></p> <p><strong>ATM - Similarity Measurement:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em><strong> </strong>files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory)<br> <br> <strong>Input:</strong><br> * Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases<br> <br> <strong>Output:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Running the experiment:</strong><br> To measure the similarity between each pair of test cases, the <em><strong>Code/ATM/Similarity/src/SimilarityMeasurement.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/Similarity/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Similarity bash measure_similarity.sh</code></pre> <p>ASTs of each project in the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directories are parsed to create pairs of ASTs containing one test case from the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> directory with another test case from the <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directory (redundant pairs are discarded). Then, all similarity measurements are saved in the <em><strong>Data/ATM/similarity_measurements.zip</strong></em> file.<br> __________________________________________</p> <p><strong>Search-based Minimization Algorithms:</strong><br> The source code for this step is in the <em><strong>Code/ATM/Search</strong></em> directory.<br> <br> <strong>Requirements:</strong><br> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <strong>Code/AMT/Search/requirements.txt</strong> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Search pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Output:</strong><br> * Results/ATM/minimization_results<br> <br> <strong>Running the experiment:</strong><br> To minimize the test suites in our dataset, the following bash script should be executed:</p> <pre><code class="language-bash">bash minimize.sh</code></pre> <p>All similarity measurements are parsed for each version of the projects, independently. Each version is run 10 times using three minimization budgets (25%, 50%, and 75%). Genetic Algorithm (GA) is run using four similarity measures, namely top-down, bottom-up, combined, and tree edit distance. NSGA-II is run using two combinations of similarity measures: top-down & bottom-up and combined & tree edit distance. The minimization results are generated in the <em><strong>Results/ATM/minimization_results</strong></em> directory.<br> __________________</p> <p><strong>Evaluate results:</strong><br> To evaluate and summarize the minimization results, run the following:</p> <pre><code class="language-bash">cd Code/ATM/Evaluation bash evaluate.sh</code></pre> <p>This will generate summarized <em>FDR</em> and execution time results (per-project and per-version) for each minimization budget, which can all be found in <strong>Results/ATM</strong>. In this replication package, we provide the final, merged <em>FDR</em> with execution time results.</p> <p><strong>_________________________________</strong></p> <p><strong>Running FAST-R experiments</strong><br> ATM was compared to <a href="https://github.com/ICSE19-FAST-R/FAST-R">FAST-R</a>, a state-of-the-art baseline, which is a set of test case minimization techniques called: <em>FAST++, FAST-CS, FAST-pw, and FAST-all</em>, which we adapted to our data and experimental setup.</p> <p><strong>Requirements:</strong><br> To run this step, Python 3.7 is required. Also, the libraries in the <em><strong>Code/FAST-R/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/FAST-R pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/FAST-R/test_methods<br> * Data/FAST-R/test_classes</p> <p><strong>Output:</strong><br> * Results/FAST-R/test_methods/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> * Results/FAST-R/test_classes/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run FAST-R experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash fast_r.sh test_methods #method level bash fast_r.sh test_classes #class level</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong> in the <em><strong>Results/FAST-R/test_methods</strong></em> and <em><strong>Results/FAST-R/test_classes</strong></em> directories.</p> <p><strong>_________________________________</strong></p> <p><strong>Running the random minimization experiments</strong><br> ATM was also compared to random minimization as a standard baseline.</p> <p><strong>Requirements:</strong> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <em><strong>Code/RandomMinimization/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/RandomMinimization pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> <em>N/A</em></p> <p><strong>Output:</strong><br> * Results/RandomMinimization/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run the random selection experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash random_minimization.sh</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <em><strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong></em> in the <em><strong>Results/RandomMinimization</strong></em> directory.</p>
Predictive Search Model of Flocking for Quadcopter Swarm in the Presence of Static and Dynamic Obstacles
<p>The folder includes experimental data for the paper titled "Predictive Search Model of Flocking for Quadcopter Swarm in the Presence of Static and Dynamic Obstacles".</p> <p>In the paper, we present a Predictive Search Model (PSM) for flocking with Heading and Speed Shared (HSS) and Heading and Speed Unshared (HSU) prediction methods. We compare the performance of PSM with Potential Field Model (PFM) in the presence of static and dynamic obstacles in simulation. Also, we validate the performance of PSM with a quadcopter swarm indoors.</p> <p>The 'simulation experiments' folder includes simulation experiment data and MATLAB scripts that can simulate the experiments and provide plots for analysis.</p> <p>The 'quadcopter experiments' folder includes quadcopter experiment data and MATLAB scripts that can simulate the experiments and provide plots for analysis.</p>
Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation
<p><strong>The unprocessed and processed benchmarking data used in the paper of "Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation"</strong></p>
Datasets generated for KA-Search: rapid and exhaustive sequence identity search of known antibodies
<p>Datasets generated for testing KA-Search in the paper "KA-Search: rapid and exhaustive sequence identity search of known antibodies".</p> <p>Includes OAS-test and the 100 randomly selected non-redundant heavy chains of therapeutics.</p>
Search Strategies for Lutetium and GEP-NETs
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. </p>
Policies used in "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"
<p>Policies used in the paper "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark". They can be tested using the software "OTTO-benchmark" available at https://github.com/auroreloisy/otto-benchmark.</p>
A Scatter Search approach for the Parallel Row Ordering Problem
<p>Instance data and full results used in https://doi.org/10.1007/978-3-031-26504-4_40</p>
The relative effectiveness of different grassland restoration methods: A systematic literature search and meta-analysis
<p>1. Active grassland restoration has gained in importance to mitigate the dramatic decline of farmland biodiversity. While there is evidence that such operations are generally effective in promoting plant diversity, little is known about the effectiveness of the different methods applied. Restoration methods can differ in intensity of seed bed preparation, seed source and technique of seed application.</p> <p>2. In this systematic literature search and meta-analysis, we screened the literature for studies of the restoration of mesic grasslands in temperate Europe. We focused on active restoration experiments that included a treatment and lasted more than three years. We evaluated the influence of restoration factors on plant species richness relative to non-restored controls.</p> <p>3. We found 187 articles that investigated the outcome of operations aimed at actively restoring mesic temperate grasslands. Most articles focused on plants, with only 9.6% dealing with other organisms (e.g. beetles, pollinating insects). Many papers had to be excluded due to incomplete data, too short study duration and/or lack of an adequate control. This resulted in 13 articles fulfilling our criteria for inclusion, yielding a total of 56 data points for the meta-analysis.</p> <p>4. Restoration actions increased plant species richness by, on average, 17.4%, compared to controls. The seed source explained a significant amount of variation in plant species richness: seeds originating from a speciose donor grassland had a positive effect. This effect was even enhanced when combined with a commercial seed mix, whereas commercial seed mixes alone had no significant effect. We did not observe any effect of other factors, such as the type of seed bed preparation or the seed application method.</p> <p>5. A seed-source obtained from species-rich grasslands seems to be key to efficient grassland restoration in mesic grasslands of temperate Europe. Even though seeds from a speciose donor grassland should be preferred over commercial seeds, associating natural and commercial seed mixes increases plant species richness. This systematic literature search further revealed two major research gaps in grassland restoration ecology: a deficit in long-term investigations as well as a deficit in studies focusing on non-plant organisms.</p>
Search strategies for new born screening for spinal muscular atrophy (SMA)
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. </p>
BLASTN results for CRISPR mini-array protospacers search
<p><strong>Supplementary file 1: BLASTN results for protospacers search</strong></p> <p>This file contains the output of the BLASTN search of the spacers in the same genomic partition. See materials and methods for more details regarding the BLASTN search. All sequences between two adjacent repeats (less than 60bp apart) were retrieved and marked with the first repeat coordinates, spacer coordinates, and “Array” flag. For all single repeat-like sequences, 30bp up/downstream as potential spacers and marked with the repeat coordinates, spacer coordinates, and “SingleUpstream” or “SingleDownstream” flags.</p>
Artifact for Paper Search-Space Pruning with Int-Splits for Faster QBF Solving
<p>This is the artifact for the paper "Search-Space Pruning with Int-Splits for Faster QBF Solving" submitted to the <a href="http://satisfiability.org/SAT23/">SAT2023</a> conference.</p>
Annexes to extensive literature searches on bromine and ochratoxin A in feed
<p>This record is a supplement to the external scientific report titled Extensive literature searches on bromine and ochratoxin A in feed.</p> <p> </p> <p><strong>Annex A – Results of the ELS </strong></p> <p>The results of the ELS for both OTA and BRs searches divided by database and combined in RIS files</p> <p><strong>Annex B –</strong> <strong>Selection for relevance</strong></p> <p>The results of the selection for relevance for both OTA and BRs searches in RIS files</p> <p><strong>Annex C –</strong> <strong>Summarising table</strong></p> <p>The summarising table and reference list for both OTA and BRs searches divided by area and combined in Excel format</p>
Search strategies for generic justification for a slot scanning, biplanar X-ray imaging system (EOS™ imaging system) for the diagnosis and assessment of orthopaedic conditions.
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. The search strategies address the following research questions:</p> <p>RQ 1 To determine the test accuracy, clinical benefits and safety of slot scanning devices (EOS system) compared to conventional X-ray imaging for diagnosis of scoliosis and evaluation or monitoring of scoliosis patients.</p> <p>RQ 2 To determine the test accuracy, clinical benefits and safety of slot scanning devices (EOS system) compared to current practice for evaluation, monitoring or diagnosis of patients with other known or suspected orthopaedic conditions.</p>
Unified MOOCs Semantic Search Engine
<p>Full dataset of Unified MOOCs</p>
Profiled Likelihoods from A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data
<p>This repository contains the profiled likelihood ratios calculated in "A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data" [arXiv:2212.07435] for the unmasked analysis. Profiled likelihood ratios are provided for each of the 30 annuli considered in the work joined over the top three quartiles of data in energy dispersion.</p> <p>The data are provided in an hdf5 archive containing 532 datasets. Line rest energies in units of GeV are provided in the `Line_Energy` dataset, which is a length 531 1-D array. Each of the remaining 531 datasets contains a 3-D array of shape (30, 2, 1001) corresponding to the profiled likelihood ratio as a function of line intensity, in units of counts/cm<sup>2</sup>/s/sr. The log of the profiled likelihood ratio for the i<sup>th</sup> line rest energy in the j<sup>th</sup> annulus may be accessed by:</p> <pre><code class="language-python">archive = h5py.File('./Archive.h5', 'r') line_energy = archive['Line_Energy'][i] data = archive['Line_' + str(i)][j] line_intensity = data[0] log_profiled_likelihood_ratio = data[1] archive.close()</code></pre> <p>For details, see <a href="https://arxiv.org/abs/2212.07435">https://arxiv.org/abs/2212.07435</a>. </p>
How doctors apply semantic components to specify search in work-related information retrieval
<p>Workplace searching is often context-specific and targets a ‘right answer’ within some<br> domain-specific aspect of the search topic. We have developed the semantic component<br> (SC) model that allows searchers to specify a search within context-specific aspects of the<br> main topic of documents. The goal of our study was to gain insight into how family practice<br> physicians at sundhed.dk, a national healthcare portal in Denmark, applied the SC model<br> to formulate queries to solve work-related search tasks. The results showed that doctors<br> used the model purposively when choosing search facets and search concepts. They were<br> relatively consistent in their use. The findings provide promising evidence of the model’s<br> potential usefulness.</p>
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.