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641 results for “data validation”
Data from: Sturgeon conservation genomics: SNP discovery and validation using RAD sequencing
Caviar-producing sturgeons belonging to the genus Acipenser are considered to be one of the most endangered species groups in the world. Continued overfishing in spite of increasing legislation, zero catch quotas and extensive aquaculture production have led to the collapse of wild stocks across Europe and Asia. The evolutionary relationships among Adriatic, Russian, Persian and Siberian sturgeons are complex because of past introgression events and remain poorly understood. Conservation management, traceability and enforcement suffer a lack of appropriate DNA markers for the genetic identification of sturgeon at the species, population and individual level. This study employed RAD sequencing to discover and characterize single nucleotide polymorphism (SNP) DNA markers for use in sturgeon conservation in these four tetraploid species over three biological levels, using a single sequencing lane. Four population meta-samples and eight individual samples from one family were barcoded separately before sequencing. Analysis of 14.4 Gb of paired-end RAD data focused on the identification of SNPs in the paired-end contig, with subsequent in silico and empirical validation of candidate markers. Thousands of putatively informative markers were identified including, for the first time, SNPs that show population-wide differentiation between Russian and Persian sturgeons, representing an important advance in our ability to manage these cryptic species. The results highlight the challenges of genotyping-by-sequencing in polyploid taxa, while establishing the potential genetic resources for developing a new range of caviar traceability and enforcement tools.
Data from: Species discovery and validation in a cryptic radiation of endangered primates: coalescent-based species delimitation in Madagascar's mouse lemurs
Implementation of the coalescent model in a Bayesian framework is an emerging strength in genetically based species delimitation studies. By providing an objective measure of species diagnosis, these methods represent a quantitative enhancement to the analysis of multilocus data, and complement more traditional methods based on phenotypic and ecological characteristics. Recognized as two species 20 years ago, mouse lemurs (genus Microcebus) now comprise more than 20 species, largely diagnosed from mtDNA sequence data. With each new species description, enthusiasm has been tempered with scientific scepticism. Here, we present a statistically justified and unbiased Bayesian approach towards mouse lemur species delimitation. We perform validation tests using multilocus sequence data and two methodologies: (i) reverse-jump Markov chain Monte Carlo sampling to assess the likelihood of different models defined a priori by a guide tree, and (ii) a Bayes factor delimitation test that compares different species-tree models without a guide tree. We assess the sensitivity of these methods using randomized individual assignments, which has been used in bpp studies, but not with Bayes factor delimitation tests. Our results validate previously diagnosed taxa, as well as new species hypotheses, resulting in support for three new mouse lemur species. As the challenge of multiple researchers using differing criteria to describe diversity is not unique to Microcebus, the methods used here have significant potential for clarifying diversity in other taxonomic groups. We echo previous studies in advocating that multiple lines of evidence, including use of the coalescent model, should be trusted to delimit new species.
Data from: Validating the power of mitochondrial metagenomics for community ecology and phylogenetics of complex assemblages
1. The biodiversity of mixed-species samples of arthropods can be characterized by shotgun sequencing of bulk genomic DNA and subsequent bioinformatics assembly of mitochondrial genomes. Here, we tested the power of mitochondrial metagenomics by conducting Illumina sequencing on mixtures of >2600 individuals of leaf beetles (Chrysomelidae) from 10 communities. 2. Patterns of species richness, community dissimilarity and biomass were assessed from matches of reads against three reference databases, including (i) a custom set of mitogenomes generated for 156 species (89% of species in the study); (ii) mitogenomes obtained by the de novo assembly of sequence reads from the real-world communities; and (iii) a custom set of DNA barcode (cox1-5′) sequences. 3. Species detection against the custom-built reference genomes was very high (>90%). False presences were rare against mitogenomes but slightly higher against the barcode references. False absences were mainly due to the incompleteness of the reference databases and, thus, more prevalent in the de novo data set. Biomass (abundance × body length) and read numbers were strongly correlated, demonstrating the potential of mitochondrial metagenomics for studies of species abundance. 4. A phylogenetic tree from the mitogenomes showed high congruence with known relationships in Chrysomelidae. Patterns of taxonomic and phylogenetic dissimilarity between sites were highly consistent with data from morphological identifications. 5. The power of mitochondrial metagenomics results from the possibility of rapid assembly of mitogenomes from mixtures of specimens and the use of read counts for accurate estimates of key parameters of biodiversity directly from community samples.
Data from: Hypothesis-driven and field-validated method to prioritize fragmentation mitigation efforts in road projects
The active field of connectivity conservation has provided numerous methods to identify wildlife corridors with the aim of reducing the ecological effect of fragmentation. Nevertheless, these methods often rely on untested hypotheses of animal movements, usually fail to generate fine-scale predictions of road crossing sites, and do not allow managers to prioritize crossing sites for implementing road fragmentation mitigation measures. We propose a new method that addresses these limitations. We illustrate this method with data from southwestern Gabon (central Africa). We used stratified random transect surveys conducted in two seasons to model the distribution of African forest elephant (Loxodonta cyclotis), forest buffalo (Syncerus caffer nanus), and sitatunga (Tragelaphus spekii) in a mosaic landscape along a 38.5 km unpaved road scheduled for paving. Using a validation data set of recorded crossing locations, we evaluated the performance of three types of models (local suitability, local least-cost movement, and regional least-cost movement) in predicting actual road crossings for each species, and developed a unique and flexible scoring method for prioritizing road sections for the implementation of road fragmentation mitigation measures. With a data set collected in <10 weeks of fieldwork, the method was able to identify seasonal changes in animal movements for buffalo and sitatunga that shift from a local exploitation of the site in the wet season to movements through the study site in the dry season, whereas elephants use the entire study area in both seasons. These three species highlighted the need to use species- and season-specific modeling of movement. From these movement models, the method ranked road sections for their suitability for implementing fragmentation mitigation efforts, allowing managers to adjust priority thresholds based on budgets and management goals. The method relies on data that can be obtained in a period compatible with environmental impact assessment constraints, and is flexible enough to incorporate other potential movement models and scoring criteria. This approach improves upon available methods and can help inform prioritization of road and other linear infrastructure segments that require impact mitigation methods to ensure long-term landscape connectivity.
Data from: Development and validation of rapid environmental DNA (eDNA) detection methods for bog turtle (Glyptemys muhlenbergii)
Bog turtles Glyptemys muhlenbergii are listed as Species of Greatest Conservation Need (SGCN) for wildlife action plans in every state it occurs and multi-state efforts are underway to better characterize extant populations and prioritize restoration efforts. However, traditional sampling methods can be ineffective due to the turtle's wetland habitat, small size, and burrowing nature. Molecular methods, such as qPCR, provide the ability to overcome this challenge by effectively quantifying minute amounts of turtle DNA left behind in its environment (eDNA). Developing such methods for bog turtles has proved difficult partly because of the high sequence similarity between bog turtles and closely-related, cohabitating species, most often wood turtles ( Glyptemys insculpta ). Additionally, substrates containing bog turtle eDNA are often rich in organics or other substances that frequently inhibit both DNA extraction and qPCR amplification. Here, we describe the development and validation of a qPCR assay, BT3, targeting the mitochondrial cytochrome oxidase I gene that correctly identifies bog turtles with 100% specificity and sensitivity when tested on 201 blood samples collected from six species over a wide geographic range. We also developed a full-process internal control employing a genetically modified strain of Caenorhabditis elegans to improve DNA extraction methods, limit false negative results due to qPCR inhibition, and measure total DNA recovery from each sample. Using the internal control, we found that DNA recovery varied by over an order of magnitude between samples and likely explains the lack of bog turtle detection in some cases. Methods presented herein are highly-specific and may offer a more cost effective, non-invasive tool to supplement bog turtle population assessments in the eastern United States. Poor or differential DNA recovery, which remains unmeasured in the vast majority of eDNA studies, significantly reduced the ability to detect bog turtle in their natural environment.
Data from: Multiplex preamplification PCR and microsatellite validation allows accurate single nucleotide polymorphism (SNP) genotyping of historical fish scales
Incorporating historical tissues into the study of ecological, conservation, and management questions can broaden the scope of population genetic research by enhancing our understanding of evolutionary processes and anthropogenic influences on natural populations. Genotyping historical and low-quality samples has been plagued by challenges associated with low amounts of template DNA and the potential for preexisting DNA contamination among samples. We describe a two-step process designed to (i) accurately genotype large numbers of historical low-quality scale samples in a high-throughput format and (ii) screen samples for preexisting DNA contamination. First, we describe how an efficient multiplex preamplification PCR of 45 single nucleotide polymorphisms (SNPs) can generate highly accurate genotypes with low failure and error rates in subsequent SNP genotyping reactions of individual historical scales from sockeye salmon (Oncorhynchus nerka). Second, we demonstrate how the method can be modified for the amplification of microsatellite loci to detect preexisting DNA contamination. A total of 760 individual historical scale and 182 contemporary fin clip samples were genotyped and screened for contamination. Genotyping failure and error rates were exceedingly low and similar for both historical and contemporary samples. Preexisting contamination in 21% of the historical samples was successfully identified by screening the amplified microsatellite loci. The potential for automation, low failure and error rates, and ability to multiplex both the preamplification and subsequent genotyping reactions combine to make the protocol ideally suited for efficiently genotyping large numbers of potentially contaminated low-quality sources of DNA.
FIGURE 13. Phylogenetic tree inferred from a in Validation of the taxon Ixodes aragaoi Fonseca (Acari: Ixodidae) based on morphological and molecular data
FIGURE 13. Phylogenetic tree inferred from a partial sequence (435 characters, 113 parsimony informative) of the 16S rRNA mitochondrial gene of 12 tick species of the Ixodes ricinus complex, using I. nipponensis as outgroup. Numbers at nodes are the support values for the major branches (bootstrap) derived from 500 replicates for maximum parsimony. Number within brackets are GenBank accession numbers.
FIGURES 7–12. Ixodes aragaoi female. 7. Gnathosoma, dorsal view. 8. Gnathosoma, ventral view. 9 in Validation of the taxon Ixodes aragaoi Fonseca (Acari: Ixodidae) based on morphological and molecular data
FIGURES 7–12. Ixodes aragaoi female. 7. Gnathosoma, dorsal view. 8. Gnathosoma, ventral view. 9. Scutum with deep punctations (arrowed). 10. Coxae I–IV, internal spur on coxa I (arrowed). 11. Spiracular plate oval. 12. Tarsus I, capsule of Haller's organ (arrowed). Abbreviations: pa, porose area. Scale-bars: 7–8, 250 µm; 9–10, 500 µm; 11, 150µm; 12, 250 µm.
FIGURES 1–6. Ixodes aragaoi male. 1 in Validation of the taxon Ixodes aragaoi Fonseca (Acari: Ixodidae) based on morphological and molecular data
FIGURES 1–6. Ixodes aragaoi male. 1. Gnathosoma, dorsal view, basis capituli rectangular (arrowed). 2. Gnathosoma, ventral view, lateral and ventral lobes (arrowed). 3. Scutum, deep punctations (arrowed). 4. Idiosoma ventral view, coxae I–IV, internal spur on coxa I similar to the female. 5. Tarsus I. 6. Haller's organ, capsule (arrowed). Abbreviations: adp, adanal plate; ap, anal plate; mp, median plate. Scale-bars:1–2, 75µm; 3–4, 500 µm; 5, 50µm; 6, 150 µm.
Philippines - Open, validated health, climate, environment and socioeconomic data
<p>The <strong>Project Climate Change, Health, and Artificial Intelligence (Project CCHAIN)</strong> dataset is a validated, open-sourced linked dataset containing 20 years (2003-2022) of climate, environmental, socioeconomic, and health dimensions at the barangay (village) level across twelve Philippine cities (Dagupan, Palayan, Navotas, Mandaluyong, Muntinlupa, Legazpi, Iloilo, Mandaue, Tacloban, Zamboanga, Cagayan de Oro, Davao).</p> <p>The full documentation can be accessed <a href="https://thinkingmachines.github.io/project-cchain">here</a>.</p> <p>The tables are designed in a way that users can <strong>choose variables that are most relevant</strong> to their focus city and use case, and <strong>link these variables</strong> to form a single dataset by merging using standard geography codes and calendar dates. This can be done using the provided <a href="https://drive.google.com/drive/folders/123MrMXSVT6149MvJ8hGoDgN1s-YlQoH_">linking notebook</a>, or offline using the user's own code.</p> <p>Here are some tips on how make most use of this dataset:</p> <ul> <li> <p><strong>Focus on one location.</strong> Starting with a detailed analysis of one location allows for a better understanding of the local dynamics, which may differ across locations.</p> </li> <li> <p><strong>Choose one health data source.</strong> Pick one of either a central or local data source. Using two different data health sources is not advised because it will lead to double/overcounting of disease cases.</p> </li> <li> <p><strong>Do not use all variables at once- do a literature review first to identify possible key variables</strong>. to identify possible key variables. More often than not, using all variables is not necessary and may even yield subpar results.</p> </li> <li> <p><strong>Check data availability</strong> on your focus location and make sure they fit the requirements of your study.</p> </li> </ul>
Using GOLD Plasma Bubble Observations for Validation of Geolocation of Plasma Irregularities by Back Propagation of the High-Rate FORMOSA7/COSMIC 2 Scintillation Data
The dataset contains files geolocation of scintillations from COSMIC-2 high rate scnPhs files using back-propagation method. The dataset was used in the paper for validation of the geolocation method. The data are in multi-column ASCII file format.
FIGURE 2. Phylogenetic results. A, Maximum likelihood tree from COI dataset rooted with Ophelia limacina. B, Maximum likelihood tree from ITS1 in Validation of three sympatric Thoracophelia species (Annelida: Opheliidae) from Dillon Beach, California using mitochondrial and nuclear DNA sequence data
FIGURE 2. Phylogenetic results. A, Maximum likelihood tree from COI dataset rooted with Ophelia limacina. B, Maximum likelihood tree from ITS1 dataset rooted according to the result for the COI dataset. Support values are shown as jackknife from parsimony analysis and bootstrap from maximum likelihood respectively separated by /. * indicates 100% values for each support measure.
FIGURE 1. The three sympatric Thoracophelia spp. from Dillon Beach. A, Thoracophelia dillonensis. B in Validation of three sympatric Thoracophelia species (Annelida: Opheliidae) from Dillon Beach, California using mitochondrial and nuclear DNA sequence data
FIGURE 1. The three sympatric Thoracophelia spp. from Dillon Beach. A, Thoracophelia dillonensis. B, Pectinate branchiae of T. dillonensis. C, Thoracophelia williamsi. D, Bifurcated branchiae with pinnules of T. williamsi. E, Thoracophelia mucronata. F, Bifurcated branchiae of T. mucronata. Scale bars all 1 mm.
Supplementary data for "Validation of a quantitative PCR assay for detection of sugar kelp (Saccharina latissima) in coastal marine sediments"
<p>Raw data for a paper in preparation: "Validation of a quantitative PCR assay for detection of sugar kelp (Saccharina latissima) in coastal marine sediments".</p><p>Contains:</p><ol><li>Spreadsheet of curated raw data used to calculated <i>S. latissima</i> COI gene copy number per gram of dry sediment for a biomass spiking experiment to determine how gene copy number correlates with known amounts of added kelp biomass.</li><li>Spreadsheet of curated raw data from a field study looking at <i>S. latissima</i> gene copy numbers in field samples from the Damariscotta River estuary in Maine, from three sites, one under a small kelp farm, one under a site with a single kelp line, and another in a control site with no known influence of kelp farming activity.</li></ol>
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
Original Data Files For LYMPROM Validation
<h1>A new validated Lymphoedema-specific Patient Reported Outcome Measure (LYMPROM<sup>©</sup>) for adults with Lymphoedema</h1> <p><span>Melanie Thomas<sup>1¶</sup>; Marie Gabe-Walters<sup>1¶</sup>; Ioan Humphreys<sup>2¶*</sup>; Alan Watkins<sup>2¶</sup></span></p> <p><span>1 Lymphoedema Wales Clinical Network,</span> <span>Swansea Bay University Health Board, Wales, United Kingdom</span></p> <p><span>2 Faculty of Medicine, Health and Life Sciences, Swansea University, Wales, United Kingdom</span></p> <p> </p> <p><span>A new lymphoedema-specific Patient Reported Outcome Measure (LYMPROM<sup>©</sup>) was developed to help patients easily report the impact of their lymphoedema and enable lymphoedema therapists to understand what matters most to patients.</span></p>
ARCH-COMP 2021: Validation Data from the Falsification Category
<p>This artifact documents the results from the falsification category of the ARCH 2021 friendly competition.</p>
AHI-CALIOP Collocated Data for Training and Validation of Cloud Masking Neural Networks
<p>Collocated data between AHI at 2km resolution (nadir) and CALIOP 1km cloud product v4.20 used for training and validating cloud identification neural networks. The main training and validation data from 2019 is stored in monthly directories, whilst the collocated dataset used to compare the NN, JMA and BoM cloud mask performances is the file "superdf.h5". All collocated data is stored as .h5 files and was built using the Python Pandas package. In this archive, the data has been stored as compressed directories for each month or as a single compressed file in the case of "superdf.h5" using tar with bzip2 compression or just bzip2 compression respectively.</p>
Raw data for Urine DNA (uDNA) as a non-lethal method for endoparasite biomonitoring: development and validation
<p>Changes in environmental conditions alter host-parasite interactions, raising the need for effective epidemiological surveillance. Developing operational, accurate, and cost-effective methods to assess individual infection status and potential for pathogen spread is a prerequisite to anticipate future disease outbreaks in wild populations. For endoparasites, effective detection of infections usually relies on host-lethal approaches, which are barely compatible with wildlife conservation objectives. Here, we used the brown trout (<i>Salmo trutta</i>) - <i>Tetracapsuloides bryosalmonae</i> host-parasite system to develop a non-lethal method for endoparasite infection detection, hereafter called "uDNA" for urine DNA. The uDNA diagnostic test is based on the amplification of endoparasite DNA from host urine. We sampled wild fish (N = 111) from eight sites, let them excrete in individual buckets filled with mineral water and performed parasite DNA amplification from water filtration. We compared the results of the uDNA diagnostic test for host infection status and parasite load to those from kidney samples (the current standard method). uDNA was sensitive in determining host infection status (even for infected hosts showing no sign of the disease), since up to 90% of fish individuals were correctly assigned to their infection status. The quantity of uDNA detected from the hosts depended on the sampling sites, suggesting a spatial variation in the parasite spread. uDNA was positively, but weakly correlated with parasite load in the kidney. This correlation depended on the severity of macroscopic lesions caused by the disease, and was negative in fish with severely damaged kidney, likely due to impaired urine excretion. The uDNA approach provides novel avenues to non-lethally infer infection parameters from wildlife populations at large spatial scales. By targeting parasite transmission stage, uDNA is also valuable to get insights on the parasite fitness and the ecological and evolutionary dynamics of this host-parasite interaction.</p>
Monte Carlo Validation Data
<p>The data required to perform the analysis outlined in <a href="https://github.com/teaghan/PET_MonteCarlo">this github repository</a>, which accompanies the paper (still to come...)</p>
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.