Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Stability (similarity) values of output maps generated using different background point sampling strategies

These tables represent the model settings and similarity values of Species Distribution Models generated using different background pointssampling strategies. The similarity values are our own newly designed way to investigate model stability and were determined by comparing an output map of an SDM fitted using real data (original map) with an output map of an SDM fitted using virtual occurrences generated from the original map.

opencc-by-4.0Apr 2023View details →

Processed NEMO output investigating glider sampling strategies

This output is related to an investigation into glider sampling strategies. The data is generated from output from a 1/48o NEMO simulation of a patch of the north-eastern Weddell Sea. Each file corresponds to data required for reproducing figures included in an associated publication, where this data is also outlined. Verbose file naming is used for easier understanding of the file contents. See rdPatmore on GitHub for code used to produce and manipulate this data.

openmit-licenseDec 2023View details →

Mini-RF S-band Radar Characterization of a Lunar South Pole-Crossing Tycho Ray: Implications for Sampling Strategies

Data behind the figures for the publication in the Planetary Science Journal.  Data is in .mat format, which is a Matlab save file which can also be read by open languages such as Python. The accompanying code, in .m format, is a Matlab code that will recreate the figures. The .m code can be read by any text editor application. All figures are also provided as pngs. Figures 7 and 8 are provided as GeoTiffs, where the first channel is S1, second channel S2, third channel S3, and the fourth channel S4 (i.e., the four Stokes parameters). Figures.zip is a zip file with all of the figures in png format. FiguresData.zip includes two files: MakeFigures.m, which is the matlab code that will recreate the figures, and RiveraValentinETAL_2024_PSJ_AccompanyingData.mat, which is that matlab data needed to recreate the figures. The .m file contains a header describing each variable in the .mat file.  GeoTiffs.zip contains two files, newton_stokes.tiff and haworth_stokes.tiff. These are GeoTiffs of Figures 7 and 8, respectively.

opengpl-3.0-or-laterAug 2024View details →

Digging for Decision Trees: A Case Study in Strategy Sampling and Learning (experimental reproduction package)

This artifact permits to reproduce the experimental results obtained with the techniques and algorithms presented in the article "Digging for Decision Trees: A Case Study in Strategy Sampling and Learning" by Carlos E. Budde, Pedro R. D'Argenio, and Arnd Hartmanns (2024). The contents include all data and software (formal models, software tools, Python & bash scripts) used in the experimental evaluation presented in §7 of the article. Detailed instructions on how to reproduce the results are bundled in the artifact. Execution has been tested in the Virtual Machine available at https://zenodo.org/records/7113223.

opencc-zeroJun 2022View details →

Improved biodiversity detection using a large-volume environmental DNA sampler with in situ filtration and implications for marine eDNA sampling strategies

Metabarcoding analysis of environmental DNA samples is a promising new tool for marine biodiversity and conservation. Typically, seawater samples are obtained using Niskin bottles and filtered to collect eDNA. However, standard sample volumes are small relative to the scale of the environment, conventional collection strategies are limited, and the filtration process is time consuming. To overcome these limitations, we developed a new large – volume eDNA sampler with in situ filtration, capable of taking up to 12 samples per deployment. We conducted three deployments of our sampler on the robotic vehicle Mesobot in the Flower Garden Banks National Marine Sanctuary in the northwestern Gulf of Mexico and collected samples from 20 to 400 m depth. We compared the large volume (~40 – 60 liters) samples collected by Mesobot with small volume (~2 liters) samples collected using the conventional CTD rosette – mounted Niskin bottle approach. We sequenced the V9 region of 18S rRNA, which detects a broad range of invertebrate taxa, and found that while both methods detected biodiversity changes associated with depth, our large volume samples detected approximately 66% more taxa than the CTD small volume samples. We found that the fraction of the eDNA signal originating from metazoans relative to the total eDNA signal decreased with sampling depth, indicating that larger volume samples may be especially important for detecting metazoans in mesopelagic and deep ocean environments. We also noted substantial variability in biological replicates from both the large volume Mesobot and small volume CTD sample sets. Both of the sample sets also identified taxa that the other did not – although the number of unique taxa associated with the Mesobot samples was almost four times larger than those from the CTD samples. Large volume eDNA sampling with in situ filtration, particularly when coupled with robotic platforms, has great potential for marine biodiversity surveys, and we discuss practical methodological and sampling considerations for future applications.

opencc-by-4.0Oct 2022View details →

FPCA - From mobile app-based crowdsourcing to crowd-trusted food price estimates in Nigeria: pre-processing and post-sampling strategy for optimal statistical inference

Timely and reliable monitoring of commodity food prices is an essential requirement for the assessment of market and food security risks and the establishment of early warning systems, especially in developing economies. However, data from regional or national systems for tracking changes of food prices in sub-Saharan Africa lacks the temporal or spatial richness and is often insufficient to inform targeted interventions. In addition to limited opportunity for [near-]real-time assessment of food prices, various stages in the commodity supply chain are mostly unrepresented, thereby limiting insights on stage-related price evolution. Yet, governments and market stakeholders rely on commodity price data to make decisions on appropriate interventions or commodity-focused investments. Recent rapid technological development indicates that digital devices and connectivity services are becoming affordable for many, including in remote areas of developing economies. This offers a great opportunity both for the harvesting of price data (via new data collection methodologies, such as crowdsourcing/crowdsensing — i.e. citizen-generated data — using mobile apps/devices), and for disseminating it (via web dashboards or other means) to provide real-time data that can support decisions at various levels and related policy-making processes. However, market information that aims at improving the functioning of markets and supply chains requires a continuous data flow as well as quality, accessibility and trust. More data does not necessarily translate into better information. Citizen-based data-generation systems are often confronted by challenges related to data quality and citizen participation, which may be further complicated by the volume of data generated compared to traditional approaches. Following the food price hikes during the first noughties of the 21st century, the European Commission's Joint Research Centre (JRC) started working on innovative methodologies for real-time food price data collection and analysis in developing countries. The work carried out so far includes a pilot initiative to crowdsource data from selected markets across several African countries, two workshops (with relevant stakeholders and experts), and the development of a spatial statistical quality methodology to facilitate the best possible exploitation of geo-located data. Based on the latter, the JRC designed the Food Price Crowdsourcing Africa (FPCA) project and implemented it within two states in Northern Nigeria. The FPCA is a credible methodology, based on the voluntary provision of data by a crowd (people living in urban, suburban, and rural areas) using a mobile app, leveraging monetary and non-monetary incentives to enhance contribution, which makes it possible to collect, analyse and validate, and disseminate staple food price data in real time across market segments. The granularity and high frequency of the crowdsourcing data open the door to real-time space-time analysis, which can be essential for policy and decision making and rapid response on specific geographic regions. <a href="https://datam.jrc.ec.europa.eu/datam/perm/news/870?rdr=1666109837893">Link to the project</a>

opencc-by-4.0Jul 2023View details →

Toileting behaviours of the UK public: insights for reducing gender bias wastewater-based epidemiology sampling strategies

Cross-sectional survey results from a toileting behaviour survey conducted between the 27th to the 28th of June 2022. Participants (n = 2109) were aged 18 years or older and were living in the UK. The survey consisted of 17 closed-ended questions, with 7 of the questions addressing specific demographic topics and 10 questions addressed toileting behaviour. The questionnaire was designed by a team consisting of environmental microbiologists, public health specialists, wastewater-based epidemiologists, and social scientists, based on the study objectives and incorporating information from previous studies on the same topic.  First, self-report questions were asked on typical frequency of urination and defecation, followed by self-reports of frequency of urination and defecation at a variety of locations including at home, at work, educational buildings, transport hubs, and in public toilets. The comfort in urination/defecation at these locations for defecation and urination was also measured. Other questions about toileting behaviour and health monitoring were measured by statements with a 5-point Likert scale (e.g., strongly disagree to strongly agree).

opencc-by-4.0Oct 2023View details →

Deep-sea ecosystem exploration and 'health check': sampling strategy and methods applied during the iAtlantic_BR10_Petrobras cruise in the Santos Basin, Southwest Atlantic

Supplementary material for the article "Deep-sea ecosystem exploration and 'health check': sampling strategy and methods applied during the iAtlantic_BR10_Petrobras cruise in the Santos Basin, Southwest Atlantic".

publicJun 2022View details →

Improved biodiversity detection using a large-volume environmental DNA sampler with in situ filtration and implications for marine eDNA sampling strategies

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publicAug 2025View details →

Data from: Sampling strategy matters: eDNA-based assessment and extrapolation of myxozoan diversity in a model stream system

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publicApr 2025View details →

Data from: An accessible metagenomic strategy allows for better characterization of invertebrate bulk samples

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dryad32/100

Data from: Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. The implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistic constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic datasets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographic representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.

opencc-zeroSep 2019View details →

opencc-by-4.0Nov 2023View details →

Metadata of sampling strategy during metabarcoding analysis (Journal publication supplement)

The table presents supplementary materials and contains metadata of sampling strategy during metabarcoding analysis of four types of substrates in two ombrotrophic bog habitats, with other experimental and environmental parameters included in analyses.

openmit-licenseApr 2024View details →

Temporal validity of software datasets for code metrics: an empirical assessment of sampling strategies

This is the repository for the scripts and data of the study "Building and updating software datasets: an empirical assessment". <h2>Data collected</h2> 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: <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> 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. <h2>Replication Kit</h2> 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>". The script comprise 5 steps: <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>

opencc-by-4.0May 2024View details →

Optimising recovery of DNA from minimally-invasive sampling methods: efficacy of buccal swabs, preservation strategy and DNA extraction approaches for amphibian studies_Dataset_Rscript

Datasets and Rscript associated with paper draft titled: "Optimising recovery of DNA from minimally-invasive sampling methods: efficacy of buccal swabs, preservation strategy and DNA extraction approaches for amphibian studies".   Abstract: Studies in evolution, ecology and conservation are increasingly based on genetic and genomic inferences. With increased focus on molecular approaches, ethical concerns about destructive or more invasive techniques need to be considered, with a push for minimally invasive sampling to be optimised. Buccal swabs have been increasingly used to collect DNA in a number of taxa, including amphibians.  However, DNA yield and purity from swabs is often low, limiting its use. In this study we compare different types of swabs, preservation method and storage, and DNA extraction technique in three case studies to assess the optimal approach for recovering DNA in anurans. Out of the five different types of swab that we tested, Isohelix MS-02 and Rapidry swabs generated higher DNA yields than other swabs. When comparing storage buffers, ethanol is a better preservative than a non-alcoholic alternative. Dried samples resulted in similar or better final DNA yields than ethanol-fixed samples if kept cool. DNA extraction via a Qiagen™ DNeasy Blood and Tissue Kit and McHale’s salting out extraction method resulted in similar DNA yields but the Qiagen™ kit extracts contained less contamination. We also found that samples produce better DNA recovery if frozen as soon as possible after collection. We provide recommendations for sample collection and extraction under different conditions, including budgetary considerations, size of individual sampled, access to cold storage facilities, and DNA extraction methodology. Maximising efficacy of all of these factors for better DNA recovery will allow buccal swabs to be used for genetic and genomic studies in a range of vertebrates.