Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,302
datasets available to search
ShareScore release 0.9.0
Dataset results
9,302 results for “DETECT”
Unsupervised detection of semantic correlations in big data
<p>simulated spin configurations from statistical mechanics models at thermal equilibrium. Further details are in our manuscript with the same title. </p>
Data for: Detection and diversity of Phytophthora species from declining Quercus suber stands using both DNA metabarcoding and soil baiting techniques
<p>This dataset on Zenodo accompanies the manuscript Salvatore <em>et al.</em> (2024), Detection and diversity of <em>Phytophthora</em> species from declining <em>Quercus suber</em> stands using both DNA metabarcoding and soil baiting techniques.</p> <p>There are two files here on Zenodo:</p> <ul> <li><code>metadata.tsv</code> - plain text table as tab-separated variables</li> <li><code>raw_data.tar.gz</code> - compressed archive of 56 paired raw FASTQ files</li> </ul> <p>This represents a subset of one complete Illumina Nano MiSeq plate run at the James Hutton Institute also containing a small number of unrelated samples using the same protocol.</p> <p>To repeat the analysis described in the paper, first install THAPBI PICT. See <a href="https://github.com/peterjc/thapbi-pict/">https://github.com/peterjc/thapbi-pict/ </a>for instructions. At the time of the paper, v1.0.16 was the current release.</p> <p>Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:</p> <pre><code> $ tar -zxvf raw_data.tar.gz</code><br><code> $ ls -1 raw_data/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code> $ cd raw_data/ $ md5sum -c MD5SUM.txt</code><br><code> $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code> $ mkdir -p intermediate/ summary/</code></pre> <p>Run the THAPBI PICT pipeline:</p> <pre><code> $ thapbi_pict pipeline -m 1s3g -f 0 -a 15 -i raw_data/ \</code><br><code> -s intermediate/ -o summary/sardinia \</code><br><code> -t metadata.tsv -u -x 8 -c 4,5,3,2,7,6</code></pre> <p>The options here are as follows:</p> <ul> <li><code>-m</code> - use the 1s3g classifier (see methods)</li> <li><code>-f</code> - set to zero to disable the fractional abundance threshold</li> <li><code>-a</code> - set a lower absolute abundance threshold</li> <li><code>-i</code> - location of the input raw data</li> <li><code>-s</code> - optional location to store intermediate files</li> <li><code>-o</code> - output stem for reports</li> <li><code>-t</code> - filename for tab-separated-variable metadata</li> <li><code>-u</code> - show unsequenced samples defined in the metadata</li> <li><code>-x</code> - which metadata column contains Illumina FASTQ filename stems</li> <li><code>-c</code> - which metadata columns to include in the report.</li> </ul> <p>This leaves the <code>-d</code> option with the default provided ITS1 database. We are NOT taking advantage of the negative controls to automatically set a blanket minimum abundance as Control-Plate-3-Mix-3-Dry-P3-c_S56_L001 sadly has over 3000 <em>Phytophthora</em> reads.</p> <p>That takes under a minute to run, and classifies most of the samples.</p> <p>Opening the output file <code>summary/sardinia_20240912_v1.0.16.ITS1.samples.1s3g.xlsx</code> in Excel or similar should show you a table resembling Table 3 in the paper, without the baiting results, but with one row per sequencing sample, and additional columns with per-sample per-species read counts etc. The similarly named reads file as one row per unique amplicon sequence variant (ASV), and columns for each sequencing sample.</p> <p>All the <em>Phytophthora</em> classifications were to species level except a single ASV from E5BTB-DILUTE-Wet-P6_S17_L001 (S3 Bultei) with 15 reads, a perfect match to partial sequences MH588088.1 and MH593844.1, isolates Y1 and Y2, which is in the THAPBI PICT database but only at genus level:</p> <pre><code>>bad82a53fff502146e7ea00cbf2d9d3e Phytophthora<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAACTTTCCACGTGAACCGTTTCAAACCAAATAGTTGGGGGTCTTGTCTGGTGGCGGCTGCTGGCTTTATTGTTGGCGGCTGCTGCTGGGTGAGCCCTATCATGGCGAGCGTTTGGGCTTCGGCCTGAGCTAGTAGCATTTCTTTTAAACCCATTCCTTAATACTGATTATACT</code></pre> <p>There are 9 samples left simply as "Unknown" (all six from S2 Buddusò, one each from S3 Bultei, S4 Nuoro, and S6 Tempio). A further two samples contained low levels of an unknown sequence in addition to knowns.</p> <p>The unknown ASV from S1BTC-DILUTE-Wet-P6_S9_L001 (S3 Bultei) looks to be a novel <em>Phytophthora</em> if real, similar to <em>P. quercina</em>:</p> <pre><code>>3cd110db0a0b8fa8b971ea6b61c53970 Phytophthora?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAAAACTTTCCACGTGAACCGTTTCAACCAATATTTTGGGGGTCTCGTCTGGCGTGCGGCTGTTGCTGTAAAAGGCGGCGGCTGTTGCTGGGTGAGCCCTATCATGGCAAACGTTTGGGCTTCGGTCTGAACAAGTAGCTCTTTTTTAAACCATTACTTATTACTGATTATACT</code></pre> <p>The unknown ASV from S4OC-DILUTE-Wet-P6_S2_L001 (S4 Nuoro) looks to be a <em>Pythium</em> (a perfect match to partial sequences MN269744 and KY822489 from uncultured clones):</p> <pre><code>>eac8c1931b4f8c57803e6ad9ffb4eb56 Pythium?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCAAAAAACTATCCACGTGAACCGTTAAGCAAAAGTCTAGTTGGCTTGTGTTGTTCGGGAGTGTGTTGGGAAGAGCTTGGAGATGTCTTCGGATATTTCGATGCCTAGTACTGGACATCCTGGCGAGCGAGTCGGCTAGCAACGAAGGTCGGGAGTTCGCTTGCGGACTGATGTGCGCTTGTCGCATGTCGGTCGAAAGGCTTGAGCAAACGGCTGATCTATTACTTTTAAACCATACCATAACTACTGATGATACT</code></pre> <p>The unknown ASV from S9OC-Wet-P5_S12_L001 (S4 Nuoro) at only 38 reads seems to be an artefact of some kind, possibly chimeric:</p> <pre><code>>74933d826f6077cd5f3dd036f894429d Artefact?<br>TAGCCGTAGGGGAACCTGCGGCTGGATCACCTCCTTTCTGGATTCGGAAGGCAGGGATCAGTGATCAGTTATCAGAACCGATTGCTGCTCCTCTTCCGAGCATCCACAACGCCAGCTCTGGCAGGAATTCTGATATCTGATATCTGGTTTCTGCGATCTGGCAACGGCGCCGCCGTCTGCGCATCCCTTCTGCCGCGTTATTCCAGAGGGCAGTGATCAGTCATCAGAACCGATGGCGGATCCGCCCCCGGCTTCACGGCGAGCGCCCGAGCCTG</code></pre> <p>The remaining unknown ASVs were likely <em>Plasmopara</em>.</p> <p> </p>
Ultra-low-power Edge Detection Based on Ferroelectric Field Effect Transistor (data and code)
Open the record for dataset details and reuse information.
Palm Detection Dataset in Ecuadorian Rainforest
Open the record for dataset details and reuse information.
BadODD: Bangladeshi Autonomous Vehicle Object Detection Dataset
<p>The dataset covers the following 9 districts in Bangladesh: Sylhet, Dhaka, Rajshahi, Mymensingh, Maowa, Chittagong, Sirajganj, Sherpur, and Khulna. Participants will encounter a wide range of road types, including towns, expressways, highways, and village roads. This diversity in locations aims to challenge algorithms to perform well across various driving contexts commonly encountered on Bangladesh roads.</p> <p> </p>
Deep-sea observatories images labeled by citizen for object detection algorithms
<div>All information is available from the original publication page: <a href="https://doi.org/10.17882/101899">https://doi.org/10.17882/101899</a>.</div>
Datasets of RNA structures for benchmarking entanglement detection and removal protocol using RNAspider and SPQR.
<p>Datasets of RNA structures for benchmarking entanglement detection and removal protocol using RNAspider and SPQR.</p>
Enhanced Anomalies Detection
Open the record for dataset details and reuse information.
Exploration of stork behaviour as an indicator for wildfire detection based on bio logging data
<p>This depository holds in a compressed folder both the dataset (in CSV and RData format) and plots used/generated during the research. Datasets and plot are named in accordance to how they were generated during the research. For a better understanding of the data, some names have been modified to better reflect their use. </p>
Exploration of Biomarkers for the Susceptibility, Early Detection and Treatment Response of Brain Tumours using Epidemiological Techniques
<p>The following resource is from Lily Andrews thesis entitled: Exploration of Biomarkers for the Susceptibility, Early Detection and Treatment Response of Brain Tumours using Epidemiological Techniques. This presents tables that were too large to place in the appendix of the thesis.</p>
Dataset 1 for Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing
<p>Datasets for the paper "Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing"</p> <p>Yue Zhanga,1, Alina Sigaevaa,1 , Arturo Elías-Llumbeta, Siyu Fana , Willem Woudstraa, Rinse de Boerc, Elkin Escobara,<br>Claudia Reyes-San-Martina, Robin Kisabacaka , Dorenda Oosterhuisa, Alan R. Gortera, Britt Coenena,c , Felipe P. Perona Martineza,<br>Geert van den Bogaartc, Peter Olingaa, and Romana Schirhagla</p>
Experiment results of the article of "Enhancing spatial domain detection in spatial transcriptomics with EnSDD"
<p>The experiment results for the reproducibility of the article of "Enhancing spatial domain detection in spatial transcriptomics with EnSDD"</p>
Dataset 2 for Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing
<p>Datasets 2 for the paper "Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing"</p> <p>Yue Zhanga,1, Alina Sigaevaa,1 , Arturo Elías-Llumbeta, Siyu Fana , Willem Woudstraa, Rinse de Boerc, Elkin Escobara,<br>Claudia Reyes-San-Martina, Robin Kisabacaka , Dorenda Oosterhuisa, Alan R. Gortera, Britt Coenena,c , Felipe P. Perona Martineza,<br>Geert van den Bogaartc, Peter Olingaa, and Romana Schirhagla</p> <p> </p>
Direct electrochemical reduction of graphene oxide thin film for aptamer-based selective and highly sensitive detection of matrix metalloproteinase 2
<p>Simple and low-cost biosensing solutions are suitable for point-of-care applications aiming to overcome the gap between scientific concepts and technological production. To compete with sensitivity and <a title="Learn more about selectivity from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/selectivity">selectivity</a> of golden standards, such as <a title="Learn more about liquid chromatography from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/liquid-chromatography">liquid chromatography</a>, the functionalization of biosensors is continuously optimized to enhance the signal and improve their performance, often leading to complex chemical assay development. In this research, the efforts are made on optimizing the methodology for <a title="Learn more about electrochemical reduction from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/electrochemical-reaction">electrochemical reduction</a> of <a title="Learn more about graphene oxide from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemistry/graphene-oxide">graphene oxide</a> to produce thin film-modified gold electrodes. Under the employed specific conditions, 20 cycles of <a title="Learn more about cyclic voltammetry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/cyclic-voltammetry">cyclic voltammetry</a> (CV) are shown to be optimal for superior electrical activation of <a title="Learn more about graphene oxide from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/graphene-oxide">graphene oxide</a> into electrochemically <a title="Learn more about reduced graphene oxide from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/reduced-graphene-oxide">reduced graphene oxide</a> (ERGO). This platform is further used to develop a <a title="Learn more about matrix metalloproteinase 2 from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/mmp2">matrix metalloproteinase 2</a> (MMP-2) biosensor, where specific anti-MMP2 <a title="Learn more about aptamers from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/aptamer">aptamers</a> are utilized as a biorecognition element. MMP-2 is a protein which is typically overexpressed in tumor tissues, with important roles in tumor invasion, metastasis as well as in tumor <a title="Learn more about angiogenesis from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/angiogenesis">angiogenesis</a>. Based on impedimetric measurements, we were able to detect as low as 3.32 pg mL<sup>−1</sup> of MMP-2 in PBS with a dynamic range of 10 pg mL<sup>−1</sup> – 10 ng mL<sup>−1</sup>. Further experiments with real blood samples revealed a promising potential of the developed sensor for direct measurement of MMP-2 in complex media. High specificity of detection is demonstrated – even to the closely related <a title="Learn more about enzyme from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/enzyme">enzyme</a> MMP-9. Finally, the potential of reuse was demonstrated by signal restoration after experimental detection of MMP-2.</p>
Parameter estimation catalogs for binary neutron star mergers detected with next-generation gravitational wave detectors
<div> <p>Next-generation gravitational wave (GW) observatories, such as the Einstein Telescope (ET) and the Cosmic Explorer, will provide access to the population of binary neutron star (BNS) mergers throughout cosmic history and yield precise parameter estimates. Here, we publish the results of a comprehensive study evaluating BNS merger detection prospects using the ET alone or in a network of current or next-generation detectors up to redshift equal to 1. We publicly release all the parameter estimation for 10 years of observations of BNSs in the form of catalogs. These catalogs are made available to the community for multi-messenger studies, multi-probe cosmology, and nuclear study to constrain the neutron star (NS) equation of state (EOS). They can be used to focus on specific events (for example golden events with high signal-to-noise ratio) or for statistical studies on the BNS populations. </p> <p>Our simulations assessed the perspectives for detecting the optical emission of BNS mergers in the era of next-generation detectors, considering how uncertainties in BNS population properties, NS mass distribution, and the EOS might affect the detection rate and parameter estimation. The study is published in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo, Hazra, Dupletsa, Branchesi et al. 2024</a> arXiv:2411.02342 (submitted to A&A).</p> </div> <h3>BNS merger rate</h3> <p>As shown in <a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4877S/abstract" target="_blank" rel="noopener">Santoliquido et al. (2021)</a>, the common envelope ejection efficiency parameter, α, determines one of the main sources of uncertainty for the number of BNS mergers per year. In order to evaluate the impact of the uncertainties of the BNS merger rate normalization on our results, we generate two catalogues of BNS mergers assuming α to be either <strong>0.5</strong> or <strong>1.0</strong>. </p> <h3>NS mass distribution</h3> <p>We draw the component masses of the NS binaries, M_1 and M_2, from two different mass distributions: <strong>Gaussian</strong> and<br><strong>uniform</strong> mass distributions. The Gaussian distribution is centred at 1.33 M⊙ with a standard deviation of 0.09 M⊙. The uniform mass distribution ranges in [1.1 M⊙, M_max], where M_max depends on the selected EOS.</p> <h3>Equation of state (EOS)</h3> <p>Since the NS EOS affects both the GW and EM signals expected from BNS mergers, we consider<br>two different EOSs, namely the <strong>APR4</strong> and <strong>BLh</strong> microscopic EOSs.</p> <h3>Detector configuration</h3> <p>Given the two values of α (0.5 and 1.0), the two mass distributions (uniform and Gaussian), and the two EOSs (BLh and APR4), we have a total of 8 different population sets, which constitute our injections for the gravitational signal analysis. For each of these datasets, we consider the following GW detector configurations:</p> <ul> <li>ET in its triangular design of 10 km arms, located in Sardinia, alone and operating together with (<strong>ET_delta_10_cryo</strong>): <ul> <li>the current ground-based network LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA, LIGO-India (<strong>LVKI</strong>) </li> <li>one L-shaped CE with 40 km arms, located in the USA (<strong>1CE</strong>)</li> <li>2 CEs, both with 40 km arms, one in the USA and one in Australia (<strong>2CE</strong>)</li> </ul> </li> <li>ET in its 2L-shaped interferometer configuration of 15 km arms misaligned at 45 deg (one located in Sardinia and the other in the Netherlands); we consider the same networks as above, using the 2L-configuration instead of the triangular one (<strong>ET_2L_15_cryo_45deg</strong>). </li> </ul> <p>We thus have eight different detector networks giving a total of 64 simulations available in this repository. </p> <h3>Catalog description</h3> <p>The parameter estimation of the injected GW signals by the various detector networks is obtained through the Fisher matrix software <strong>GWFish</strong> (<a href="https://ui.adsabs.harvard.edu/abs/2023A%26C....4200671D/abstract" target="_blank" rel="noopener">Dupletsa et al. 2023</a>). The Fisher analysis method approximates the likelihood with a multivariate Gaussian distribution. All the parameters [M_1, M_2, dL, ι, RA, DEC, Ψ, phase, tc, Λ_1, Λ_2] are considered for the Fisher matrix derivation. The uncertainties on parameters coming from the covariance matrix (the inverse of the Fisher matrix) are given at 1σ. We implement a duty cycle of 85% for each of the L-shaped detectors, and for each of the three nested detectors composing the triangle. </p> <ul> <li><strong>Signals_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the parameters describing a GW event and the corresponding network signal-to-noise ratio (SNR) <ul> <li><strong>mass_1: </strong>primary mass of the binary in [Msol] (in detector frame) (M_1)</li> <li><strong>mass_2:</strong> secondary mass of the binary in [Msol] (in detector frame) (M_2)</li> <li><strong>luminosity_distance:</strong> the luminosity distance of the merger in [Mpc]</li> <li><strong>dec:</strong> declination angle in [rad]. It varies in [−𝜋/2,+𝜋/2]</li> <li><strong>ra:</strong> right ascension in [rad]. It varies in [0,2/𝑝𝑖]</li> <li><strong>theta_jn:</strong> the angle between the line of observation and the total angular momentum (orbital, spin and GR corrections) of the binary [rad] (it reduces to the so-called inclination angle or <strong>iota</strong> if the spin component is absent); it ranges in [0,𝜋]</li> <li><strong>psi:</strong> the polarization angle in [rad]; it ranges in [0,𝜋]</li> <li><strong>geocent_time:</strong> merger time as GPS time in [s]</li> <li><strong>phase:</strong> the initial phase of the merger in [rad]; it ranges in [0,2𝜋]</li> <li><strong>redshift: </strong>the redshift of the merger</li> <li><strong>lambda_1: </strong>dimensionless tidal polarizabilty of primary component</li> <li><strong>lambda_2:</strong> dimensionless tidal polarizabilty of secondary component</li> <li><strong>network_SNR:</strong> network SNR for a the given event</li> </ul> </li> <li><strong>Errors_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the <div> <div>parameter errors for each event. The first column is <strong>network_SNR</strong>, the following columns repeat the injected parameters as above and the relative errors <strong>err_<em>{parameter}</em></strong><em>. </em>The last column is the error on sky localisation (<strong>err_sky_location</strong>) at 90% credible interval. </div> </div> </li> </ul> <h3>Further details </h3> <p>Further details on the assumptions we made to produce these catalogs can be found in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo et al. 2024</a>, while further details on GWFish can be found on <a href="https://colab.research.google.com/github/janosch314/GWFish/blob/main/gwfish_tutorial.ipynb" target="_blank" rel="noopener">this tutorial</a>. We also provide the jupyter notebook <strong>paper_plots.ipynb</strong>, to reproduce Figs. 10, 11, 13, D.1, D.5, D.6. </p>
One-Step Photochemical Immobilization of Aptamer on Graphene for Label-Free Detection of NT-proBNP
<p>A novel photochemical technological route for one-step functionalization of a graphene surface with an azide-modified DNA aptamer for biomarkers is developed. The methodology is demonstrated for the functionalization of a DNA aptamer for an N-terminal B-type natriuretic peptide (NT-proBNP) heart failure biomarker on the surface of a graphene channel within a system based on a liquid-gated graphene field effect transistor (GFET). The limit of detection (LOD) of the aptamer-functionalized sensor is 0.01 pg/mL with short response time (75 s) for clinically relevant concentrations of the cardiac biomarker, which could be of relevance for point-of-care (POC) applications. The novel methodology could be applicable for the development of different graphene-based biosensors for fast, stable, real-time, and highly sensitive detection of disease markers.</p>
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
<h2>README</h2> <p>The dataset consists of the following components:<br> <br>- `<strong>prompts.txt</strong>`: This file contains the prompts used for large language models.<br> <br>- `<strong>Dataset</strong>` directory: includes general information about the dataset. Specifically, the `dataset.xlsx` file lists flaky and non-flaky tests, along with their root causes and fix types.<br> <br>- `<strong>Full</strong>` directory contains two subdirectories: `Flaky` and `Non-flaky`. Each of these directories is organized by individual GitHub organization projects, with each project having its list of repository subdirectories. These subdirectories are further divided into “issues” and “pull requests” (PRs).</p> <p><br>- `<strong>Method</strong>` level subdirectory has a similar structure but contains extracted code snippets at the method level instead of full code listings. The `code.diff` file is copied over and left unaltered. </p> <p><br>- <strong>Issue Directories (IRs):</strong> Named with an `issueID` template, each issue directory contains a `log.issue` file that includes the extracted description, comments, and metadata.<br> <br>- <strong>PR Directories (PRs)</strong>: Named using the `prID` template, each PR directory contains the text, comments, and metadata in the `pr.log` file. The text of the associated issue is stored in the `log.issue` file. Code listings are stored in a file with the `.bug` suffix, while the corresponding fixed version is in a `.fix` file. The `code.diff` file contains the patch that transforms the `.bug` version into the `.fix` version.</p> <p><br><strong>Additional notes:</strong><br>Issues with associated pull requests in `dataset.xlsx` are combined into the pull request directory template. If two pull requests are listed for a row, a PR directory is created for each. Due to updates in the extended dataset, some repositories have been renamed or archived, meaning the current repository directory names in `Dataset` will include both the previous and new names if it has been changed (e.g., a repository previously saved as Qiskit/qiskit-terra may now be saved as Qiskit/qiskit following the renaming from qiskit-terra to qiskit).</p> <h2>Directory Structure:</h2> <p><br>├── prompts.txt<br>├── Dataset/<br> └── dataset.xlsx<br>├── Full/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-Flaky/<br> └── <Organization>/<Repository>/...<br>├── Method/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-flaky/<br> └── <Organization>/<Repository>/...</p> <p> </p>
Figure 3 in Detection and characterization of zoonotic Bartonella spp. in rodents and shrews ectoparasites from Kigoma and Morogoro regions, Tanzania
Figure 3: Fleas' species identified from rodents, as photographed using a Zeiss Primor Star Axiocam ERc 5S microscope. Source: Authors.
Figure 5 in Detection and characterization of zoonotic Bartonella spp. in rodents and shrews ectoparasites from Kigoma and Morogoro regions, Tanzania
Figure 5: Median-joining network showing the evolutionary relationships and likely ancestral networks among Bartonella haplotypes based on the 379-bp sequence of gltA gene from fleas (FTZ), ticks (TTZ), and lice (LTZ) hosts.
Figure 4 in Detection and characterization of zoonotic Bartonella spp. in rodents and shrews ectoparasites from Kigoma and Morogoro regions, Tanzania
Figure 4: Phylogenetic tree showing the relatedness of the Bartonella gltA gene sequences detected from rodents and shrews ectoparasites; fleas (FTZ), lice (LTZ), and ticks (TTZ) along with reference sequences from the GenBank database. The phylogenetic tree was constructed using the maximum likelihood method. The tree with the highest log likelihood (−1773.15) is shown. Evolutionary analyses were conducted in MEGA 11 (2021). The detected Bartonella genotypes and their sources in this study are indicated by nodes of different colors.
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.