Find research datasets worth reusing

opencc-zeroJul 2021View details →

Data from: Can conflicting selection from pollinators and nectar-robbing antagonists drive adaptive pollen limitation? A conceptual model and empirical test

Pollen limitation is widespread, despite predictions that it shouldn't be. We propose a novel mechanism generating pollen limitation: conflicting selection by pollinators and antagonists on pollinator attraction traits. We introduce a heuristic model demonstrating antagonist-induced adaptive pollen limitation, and present a field study illustrating its occurrence in a wild population. For antagonist-induced adaptive pollen limitation to occur, four criteria must be met: 1) correlated attraction of pollinators and antagonists, 2) greater response by antagonists than pollinators to altered investment in attraction traits, 3) reduced investment in pollinator attraction leading to pollen limitation, and 4) higher fitness for plants with reduced investment in pollinator attraction. We surveyed nectar robbery and reproductive output for 109 Odontonema cuspidatum (Acanthaceae) plants in a pollen-limited population over two years and used experimental floral arrays to evaluate how flower number affects pollination and nectar robbery. Both pollinators and nectar robbers preferred larger floral displays, and nectar robbery reduced reproductive output, suggesting conflicting selection. Survey and experimental data agreed closely on the optimum flower number under antagonist-induced pollen limitation; this number was substantially overrepresented in the population. While criteria for antagonist-induced adaptive pollen limitation are restrictive, the necessary conditions may often be realized. Considering interactions beyond the plant-pollinator dyad illuminates previously overlooked mechanisms generating pollen limitation.

opencc-zeroMar 2022View details →

Empirical food webs of 12 tropical reservoirs in Singapore

We present 12 food webs from tropical reservoir communities in Singapore, and summarise the topology of each with widely used network indices (e.g., connectance, link density). Each reservoir was surveyed over 4–6 sampling occasions, during which, representative animal groups (i.e., fish species, and taxonomic/functional groups of zooplankton and benthic macroinvertebrates) and all likely sources of primary production (i.e., macrophytes, periphyton, phytoplankton, and riparian terrestrial plants) were collected. We determined and measured gut content in fishes and bulk isotope (d13C and d15N) profiles of the animals collected, which contributed to estimating the relative strength of trophic relationships using Bayesian mixing models. We document our protocol here, alongside a script in the R programming language for executing data management/analyses/visualisation procedures used in our study. This data can be used to glean insights into trends in inter- and intra-specific or guild interactions in analogous freshwater lake habitats.

opencc-by-4.0Jul 2021View details →

TCTracer: Establishing Test-to-Code Traceability Links Using Dynamic and Static Techniques - Evaluation Data - Empirical Software Engineering 2021

This repository provides the data artefacts for the experiments conducted using our tool TCTracer for the journal paper "TCTracer: Establishing Test-to-Code Traceability links Using Dynamic and Static Techniques" as submitted to the Empirical Software Engineering journal in 2021.

opencc-by-4.0Dec 2019View details →

A semi-empirical model of radiolytic gas bubble formation and evolution during criticality excursions in uranyl nitrate solutions for nuclear criticality safety assessment

Raw data used to construct the figures found in the journal paper "A semi-empirical model of radiolytic gas bubble formation and evolution during criticality excursions in uranyl nitrate solutions for nuclear criticality safety assessment".

opencc-by-4.0Oct 2021View details →

Data and code for the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration

Data and code for the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration accepted for publication to Earth and Space Science in October 2021. The dataset contains 7 month of total losses (transmitted - received power levels) and retrieved water vapor density from a 4.87 km long full-duplex E-band commercial microwave link (CML) operating at 73.5 and 83.5 GHz in Prague, CZ. The CML was operated as a part of a mobile phone backhaul. Furthermore, observations of air temperature, and air relative humidity from sites close to the CML end nodes are provided. Finally, theoretical gaseous attenuation calculated from the air temperature and relative humidity is included as a part of the dataset. Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. All time series are regular and have 5-min temporal resolution. Metadata are stored in text files. The code is in a form of R Markdown files and html notebooks. Results presented in the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration and in its Supporting information are fully reproducible using this dataset.

opencc-by-4.0Oct 2021View details →

Package Quality for Users and Contributors: An Empirical Study of the npm Ecosystem

Dataset for the submission "Package Quality for Users and Contributors: An Empirical Study of the npm Ecosystem"

opencc-by-4.0Oct 2022View details →

Comparative Accuracies of Models for Drag Prediction During Geomagnetically Disturbed Periods: A First Principles Model versus Empirical Models

This dataset contains observational and TIEGCM simulation data used for a manuscript that is being submitted to the Space Weather journal. The abstract for the study follows: We examine the accuracy of density prediction by the first principals model Thermosphere Ionsosphere Electrodynamics General Circulation Model (TIEGCM) developed by the National Center for Atmospheric Research and compare it to the accuracy of three empirical models: Jacchia 71, the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Extended 2000 (NRLMSIS), Jacchia 1971 and Jacchia-Bowman 2008. Comparisons are made for three large storms: the October 2003 storm, the March 2013 storm, and the March 2015 storm. To evaluate the accuracy of these models we use tracking data for nine space objects in low earth orbit (three for each storm). Additionally, and evaluate the accuracy of the TIEGCM and NRLMSIS with data from high precision accelerometers on the Challenging Minisatellite Payload (CHAMP) and Gravity field and Circulation Explorer (GOCE) satellites. The goal is to assess the use of a first principles model as a potential tool for forecasting satellite drag during large magnetic storms. We find that the TIEGCM accuracy is substantially better than for the Jacchia 71 and NRLMSIS models. The accuracies of the TIEGCM and JB2008 models are similar, but overall the TIEGCM is more accurate. We found smaller mean percentage differences for TIEGCM versus CHAMP than for NRLMIS for the Halloween Storm and smaller differences than results published for JB2008 and the assimilative model HASDM. The empirical models are at present more practical for operational purposes, but the first principles TIEGCM was developed as a research model and with a greater focus on operational use offers the potential for improved utility during stressing conditions.

opencc-by-4.0Oct 2022View details →

An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems

Replication package for the manuscript entitled "An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems". See README.md file for details about how to use the package.

opencc-zeroNov 2022View details →

Faster‐haplodiploid evolution under divergence‐with‐gene‐flow: Simulations and empirical data from pine‐feeding hymenopterans

Although haplodiploidy is widespread in nature, the evolutionary consequences of this mode of reproduction are not well characterized. Here, we examine how genome-wide hemizygosity and a lack of recombination in haploid males affects genomic differentiation in populations that diverge via natural selection while experiencing gene flow. First, we simulated diploid and haplodiploid "genomes" (500-kb loci) evolving under an isolation-with-migration model with mutation, drift, selection, migration, and recombination; and examined differentiation at neutral sites both tightly and loosely linked to a divergently selected site. So long as there is divergent selection and migration, sex-limited hemizygosity and recombination cause elevated differentiation (i.e., produce a "faster-haplodiploid effect") in haplodiploid populations relative to otherwise equivalent diploid populations, for both recessive and codominant mutations. Second, we used genome-wide SNP data to model divergence history and describe patterns of genomic differentiation between sympatric populations of Neodiprion lecontei and N. pinetum, a pair of pine sawfly species (order: Hymenoptera; family: Diprionidae) that are specialized on different pine hosts. These analyses support a history of continuous gene exchange throughout divergence and reveal a pattern of heterogeneous genomic differentiation that is consistent with divergent selection on many unlinked loci. Third, using simulations of haplodiploid and diploid populations evolving according to the estimated divergence history of N. lecontei and N. pinetum, we found that divergent selection would lead to higher differentiation in haplodiploids. Based on these results, we hypothesize that haplodiploids undergo divergence-with-gene-flow and sympatric speciation more readily than diploids.

opencc-by-4.0Nov 2022View details →

Bearing capacity semi-empirical methods inputs for optimization

These datasets presents inputs for bearing capacity prediction methods. These methods are four well-known semi-empirical models for predicting bearing capacity of piles. The data was collected from the works of Lobo (2005), Vianna (2000) and Jr. (1988) and includes load tests and SPT measures taken from severam Brazilian regions.  The suffix 'DQ' and 'MH', 'Av' and 'Tx' represents which method this input was obtained from, corresponding respectively to Decourt and Quaresma (1978) and Meyerhof (1983). The columns indexes represents: N_pile - Pile number (for reference); N_l - SPT result for the pile lenght; N_p - SPT result for the pile tip L - pile lenght; D - pile diameter; U - pile perimeter; A - pile section area Qu - Pile bearing capacity, obtained through NBR 6122 load test. When using this dataset, please cite the following paper: //<a href="http://soilsandrocks.com/sr-2021-074921">soilsandrocks.com/sr-2021-074921</a> DOI: 10.28927/SR.2021.074921

opencc-by-4.0Nov 2022View details →

Data for How to estimate whether preferential trade agreements contribute to international environmental impact shifting. A new methodology and empirical illustration for Switzerland

Raw and processed data for the paper "How to estimate whether preferential trade agreements contribute to international environmental impact shifting. A new methodology and empirical illustration for Switzerland" The raw trade data file is named TradeDataAggregated.rds The raw greenhouse gas footprint data is contained in Output_all.xlsx

opencc-by-4.0Oct 2022View details →

Appendix of Empirical Study of Co-Renamed Identifiers

This data contains four CSVs: Rename.csv, Rename_Ignore_Inflection.csv, Relation.csv, Relation_Ignore_Inflection.csv.   Rename.csv and Rename_Ignore_Inflection.csv contain renamings used in our study. The difference between these CSVs is whether we ignored inflections.  Each row represents a renaming. Columns descriptions are below. <ul> <li>repository: repository name</li> <li>commit: commit hash</li> <li>filepath: location of the renamed identifier</li> <li>oldname: identifier name before the renaming</li> <li>newname: identifier name after the renaming</li> <li>type: identifier type (Class, Method, Attribute, Parameter, Variable)</li> <li>operational_chunk: operational chunks of the renaming</li> <li>meaningful_rename_set_id: meaningful rename set IDs (independent for each repository)</li> </ul>   Relation.csv and Relation_Ignore_Inflection.csv contain relationships detected in our study. The difference between these CSVs is whether we ignored inflections.  Each row represents a relationship between renamed identifier 1 and renamed identifier 2. Columns descriptions are below. <ul> <li>repository: repository name</li> <li>meaningful_rename_set_id: meaningful rename set IDs (independent for each repository)</li> <li>commit: commit hash</li> <li>relation: type of relationship</li> <li>file{1,2}: location of the identifier</li> <li>oldname{1,2}: identifier name before the renaming</li> <li>newname{1,2}: identifier name after the renaming</li> <li>type{1,2}: identifier type (Class, Method, Attribute, Parameter, Variable)</li> </ul>

opencc-by-4.0Dec 2022View details →

Data set for EMPIR project MeDDII paper bmt-2022-0039

Data set for EMPIR project MeDDII paper https://doi.org/10.1515/bmt-2022-0039 In-line measurements of the physical and thermodynamic properties of single and multicomponent liquids

Voting Behaviour - Knowledge from empirical studies

This file contains primary data for our study that compares voting behaviour under first-past-the-post, evaluative voting, approval voting and D21 – Janeček method on the primary dataset consisting of 31 elections in the Czech Republic and secondary dataset of 11 elections from various countries. Our analysis aims to explore how voters use multiple positive and negative votes and what effects these voting rules have on different types of candidates. We also focus on strategic voting and on the factors influencing the number of votes cast by a voter. Our data show significant disparities in use of negative votes under voting methods offering more resources than FPTP but not in the positive votes. Furthermore, there are more misaligned voters under those methods than under plurality voting, however the ratio of strategic votes is comparable. Last but not least, we find that these methods favour medium and unknown candidates instead of polarising and unpopular candidates like plurality voting.

Replication package for the Helm charts empirical study

Helm Charts for Kubernetes Applications: Evolution, Outdatedness and Security Risks This repository represents a replication package for our MSR study on Helm charts. This replication package requires Python 3.5+ to be installed, and all the dependencies listed in ``requirements.txt``. They can be automatically installed using ``pip install -r requirements.txt``.  These experiments were executed on a Linux Ubuntu OS. This replication package contains three folders: - notebooks: contains notebooks where we analyze data.  - figures: contains figures saved from the notebooks - datasets: contains all datasets required To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``. The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.

opencc-by-3.0Nov 2020View details →

Dataset related to the publication "Simulation of pressure-induced cavity deformation – the 18SIB04 Quantumpascal EMPIR project"

The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format.  The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication.  The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.

Replication package for "Method Chaining Redux: An Empirical Study of Method Chaining in Java, Kotlin, and Python"

This dataset includes scripts and data files used to generate all analysis and results from the paper.  A README.md file is included for details on using the scripts. The dataset is quite large.  It is broken down into three archives.  All scripts are in replication-pkg.zip and the other 2 files only contain data.  So if you want to just inspect the analysis, you only need that single zip. If you grab the data-cached.zip file and extract it, it will need around 3GB of space.  This is the processed dataset stored in Parquet files.  Use this if you want to just recreate the tables/figures from the paper. If you want to make changes to the analyses, you will need the raw data in data-raw.zip.  This will need around 29GB of space once extracted.  If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 22GB of space.

DATASET - On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code

Study package containing raw and analyzed data from the work entitled "On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code". The package comprises: 1) a summary of the information extracted from all papers mentioned in the Background; 2) the source code snippets used in the three studies; 3) the consent and characterization forms distributed to the participants; 4) the raw data, the aggregated data and other material generated with the collected data.

openother-closedNov 2021View details →

What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study

What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study   This repository contains data and code for the paper <a href="https://arxiv.org/abs/2110.04845">What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study</a>. We hope that this work will spur further research on understanding sentence--sentence relatedness, methods of sentence representation, measures of semantic relatedness, and their applications. Citing our work Please use the following BibTex entry to cite us if you use our dataset or any of the associated analyses: <blockquote>@inproceedings{abdalla2023makes,       title={What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study},       author={Abdalla, Mohamed and Vishnubhotla, Krishnapriya and Mohammad, Saif M.},       year={2023},       address = {Dubrovnik, Croatia},       publisher = "Association for Computational Linguistics",       booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume" }</blockquote> Dataset Description The dataset consists of 5500 English sentence pairs that are scored and ranked on a relatedness scale ranging from 0 (least related) to 1 (most related). Why Semantic Relatedness? Closeness of meaning can be of two kinds: semantic relatedness and semantic similarity. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation, whereas relatedness accounts for all of the commonalities that can exist between two sentences. Semantic relatedness is central to textual coherence and narrative structure. Automatically determining semantic relatedness has many applications such as question answering, plagiarism detection, text generation (say in personal assistants and chat bots), and summarization. Prior NLP work has focused on semantic similarity (a small subset of semantic relatedness), largely because of a dearth of datasets. In this paper, we present the first manually annotated dataset of sentence--sentence semantic relatedness. It includes fine-grained scores of relatedness from 0 (least related) to 1 (most related) for 5,500 English sentence pairs. The sentences are taken from diverse sources and thus also have diverse sentence structures, varying amounts of lexical overlap, and varying formality. Comparative Annotations and Best-Worst Scaling Most existing sentence-sentence similarity datasets were annotated, one item at a time, using coarse rating labels such as integer values between 1 and 5 @ representing coarse degrees of closeness. It is well documented that such approaches suffer from inter- and intra-annotator inconsistency, scale region bias, and issues arising due to the fixed granularity. The relatedness scores for our dataset were, instead, obtained using a comparative annotation schema. In comparative annotations, two (or more) items are presented together and the annotator has to determine which is greater with respect to the metric of interest. Specifically, we use Best-Worst Scaling, a comparative annotation method}, which has been shown  to produce reliable scores with fewer annotations in other NLP tasks. We use scripts from https://saifmohammad.com/WebPages/BestWorst.html to obtain relatedness scores from our annotations. Loading the Dataset - The sentence pairs, and associated scores, are in the file sem_text_rel_ranked.csv in the root directory. The CSV file can be read using: <pre><code class="language-python">python import pandas as pd str = pd.read_csv('sem_text_rel_ranked.csv') row = str.loc[0] sent1, sent2 = row['Text'].split("\n") score = row['Score']</code></pre> - Relevant columns:   - Text: Sentence pair, separated by the newline character.   - Score: The semantic relatedness score between 0 and 1. - Additionally:   - the SourceID column indicates the source dataset from which the sentence pair was drawn (see Table 2 of our paper)   - The SubsetID column indicates the sampling strategy used for the source dataset   - and the PairID is a unique identifier for each pair that also indicates its Source and Subset. Raw Annotations from Amazon Mechanical Turk - The `mturk_data/` subdirectory provides the raw MTurk annotations obtained with our comparative annotation setup. - Each row of `mturk_data/bws_annotations.csv` consists of four sentence pairs along with human annotations for the most related (column `BestItem`) and the least related (column `WorstItem`) pair. - File `mturk_data/id2sents.csv` pairs each sentence pair with the corresponding SourceID, SubsetID, and PairID that indicates the source dataset (see Table 2 of our paper). - See file `mturk_data/task_intructions.txt` for the instructions provided to annotators for our task. Datasheet for STR-2022 The datasheet for our dataset is in the document `STR2022-datastatement.pdf` in the root folder of this repository. Ethics Statement Any dataset of semantic relatedness entails several ethical considerations. We talk about this in Section 8 of our paper. Creators - <a href="https://www.cs.toronto.edu/~msa/index.html">Mohamed Abdalla</a> (University of Toronto) - <a href="https://priya22.github.io/">Krishnapriya Vishnubhotla</a> (University of Toronto) - <a href="http://saifmohammad.com/">Saif M. Mohammad</a> (National Research Council Canada) Contact: msa@cs.toronto.edu, vkpriya@cs.toronto.edu, saif.mohammad@nrc-cnrc.gc.ca