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Data of FigS1, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS1, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS1.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains three files in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2-1-3 .csv), three files in sps-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2-1-3 .sps), all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M1.pdf) and one in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M.txt).</p> <p> </p>
UK RSE Conference 2022 Walkthrough - MLOps for RSEs - Sample Data
<p>This record is intended as sample data for a Walkthrough at the UK RSE 2022 Conference titled "MLOps for RSEs". The code that uses this sample dataset can be found on <a href="https://github.com/informatics-lab/ukrse_2022_mlops_walkthrough">GitHub</a>. There are 2 parts based on the sample problems in the walkthrough</p> <ul> <li>Classifying wind rotor events</li> <li>Clustering weather regimes</li> </ul> <p><strong>Rotors Dataset</strong></p> <p>This dataset is intended as a machine learning dataset, to train a model to predict the occurrence of turbulent wind gusts called "rotors". These are wind gusts happening on the leeward side of mountains. When they occur near an airfield, this can be hazardous from aviation operations. This data is intended to be used with the code on the Met Office Data Science Community of Practice GitHub repository.</p> <p>Files:</p> <ul> <li>2021_met_office_aviation_rotors.csv - Raw dataset</li> <li>2021_met_office_aviation_rotors_preprocessed.csv - Preprocessed dataset ready for machine learning</li> <li>rotors_catalog.yml - Intake Catalog file for the rotors dataset.</li> </ul> <p>More Information:</p> <ul> <li>MO Data Science Community of Practice GitHub - https://github.com/MetOffice/data_science_cop/tree/master/challenges/2021_falklands_rotors </li> <li>Met Office Youtube - What are rotors? https://www.youtube.com/watch?v=jgSZG9SqN_s <ul> <li>What are Lee Waves? https://www.metoffice.gov.uk/weather/learn-about/weather/types-of-weather/wind/lee-waves\</li> </ul> </li> </ul> <p><strong>Weather Regime Clustering</strong></p> <p>This dataset is a UK and North Atlantic cutout of the Mean Sea-level Pressure (MSLP) field in <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5">ERA5 reanalysis dataset</a> produced by ECMWF. This dataset is used for demonstrating an unsupervised learning pipeline.</p> <p>Files:</p> <ul> <li><a href="https://zenodo.org/api/files/58a7da5c-82bf-4d65-92d6-dea0e2efb0e3/era5_mslp_UK_2017_2020.nc">era5_mslp_UK_2017_2020.nc</a> - Gridded dataset of ERA5 reanalysis data.</li> </ul>
SSR data of Plutella xylostella samples collected from Southern China and Southeast Asia
<p>Genetic makeup of insect pest is informative for source-sink dynamics, spreading of resistant genes, and effective management. However, collecting samples from geographical populations without considering temporal resolution and calculating parameters related to historical gene flow may not capture contemporary genetic pattern and metapopulation dynamics of highly dispersive pests. <em>Plutella xylostella</em> (L.), the most widely distributed Lepidopteran pest that developed resistance to almost all current insecticides, migrates heterogeneously across space and time. To investigate its real-time genetic pattern and dynamics, we executed four samplings over two consecutive years across Southern China and Southeast Asia, and constructed population network based on contemporary gene flow. Across 48 populations, genetic structure analysis identified two differentiated insect swarms, of which the one with higher genetic variation was replaced by the other over time. We further inferred gene flow by estimation of kinship relationship and constructed migration network in each sampling time. Interestingly, we found mean migration distance at around 1000 km. Such distance might have contributed to the formation of step-stone migration and migration circuit over large geographical scale. Probing network clustering across sampling times, we found a dynamic metapopulation of <em>P. xylostella</em> with more active migration in spring than in winter, and identified some regions are consistent sources (e.g., Yunnan in China, Myanmar and Vietnam) while several others are persistent sinks (e.g., Guangdong and Fujian in China) in its overwintering regions. Rapid turnover of insect swarms and highly dynamic metapopulation highlight the importance of temporal sampling and network analysis in investigation of source-sink relationships and thus effective pest management.</p>
FIGURE 3. Dendrogram generated from PATN analyses using Czekanowski metric association measures the dataset comprising eight samples and 133 in Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data
FIGURE 3. Dendrogram generated from PATN analyses using Czekanowski metric association measures the dataset comprising eight samples and 133 morphological characters. Classification strategy set at flexible UPGMA agglomerative hierarchical fusion technique with Beta = -0.10.
FIGURE 1. Dendrogram generated from PATN analyses using Gower association measures the dataset comprising 21 samples and 133 in Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data
FIGURE 1. Dendrogram generated from PATN analyses using Gower association measures the dataset comprising 21 samples and 133 morphological characters. Classification strategy set at flexible UPGMA agglomerative hierarchical fusion technique with Beta = -0.10.
Sample Deadlock Bound Data
<p>Sample deadlock bound data for ongoing PhD Thesis work.</p>
Design 3D CAD data of an oversized-sample 35 GHz EPR resonator with an elevated Q value
<pre>Supplemental data for the manuscript “Design and performance of an oversized 35 GHz EPR resonator with an elevated Q value” General information The CAD dimensions match the dimension of the manufactured resonator/probehead. Not depicted in the CAD file are the modulation coils (the modulation coil holders on the sides of the cavity block are shown) and the mechanical connection between the coupling screw on top flange of the resonator and the movable coupling piston at the bottom. Autodesk Inventor files The assembly of the whole resonator is stored as “Qband_CW_Probenkopf”. All dependent subassemblies and parts can be found in the folder “Q_band_CW_resonator_Inventor”. 3D files for Open source software The complete resonator is stored in the file “Qband_CW_Probenkopf.stl”. Since in this file all resonator components are merged into a single unit and cannot be depicted on their own, all resonator components are also stored seperately as .obj-files in the folder “Qband_CW_Probenkopf_obj_format”.</pre>
The derived data in manuscript Oblique impact adjacent to Chang'E-5 landing site: Fine-scale analysis and implication on the provenance of returned samples
<p>This website contains the derived data in the <em>manuscript <span>Oblique impact adjacent to Chang’E-5 landing site: </span><span>F</span><span>ine-scale analysis and implication on the provenance of returned samples </span></em>by <span>Wenhui Wu</span><span>, </span><span>Zhaopeng Chen</span><span>, Xin Ren</span><span>, Dawei Liu</span><span>,</span><span> </span><span>Xingguo Zeng, Yuan Chen, Wangli Chen, Wei Yan, Bin Liu,Xiaoxia Zhang, Jianjun Liu</span> for <em><span>Journal of Geophysical Research: Planets</span></em></p>
Data for SMC UV Expanded Sample paper
<p>Extinction curves used for the analysis presented in the paper titled "" by Gordon et al. (2024, ApJ, in press).</p>
Raw data and correlation analysis of physiological data sampled from Atlantic halibut (Hippoglossus hippoglossus) for the development of a PBPK model
<p>Raw data sampled from Atlantic halibut <em>(Hippoglossus hippoglossus)</em> for the characterization of physiological parameters for the development of a species-specific physiology-based pharmacokinetic (PBPK) model. Additionally, the document containes imputed data for a PCA analysis and correlation coefficients and the related p-values from a correlation analysis. </p>
A database of Defra statutory biodiversity metric unit values for terrestrial habitat samples across England, with plant, butterfly and bird species data
<p>Policies requiring biodiversity no net loss or net gain as an outcome of environmental planning have become more prominent worldwide, catalysing interest in biodiversity offsetting as a mechanism to compensate for development impacts on nature. Offsets rely on credible and evidence-based methods to quantify biodiversity losses and gains. Following the introduction<span> of the United Kingdom's Environment Act in November 2021, all new developments requiring planning permission in England are expected to demonstrate a 10% biodiversity net gain from 2024, calculated using the statutory biodiversity metric framework (Defra, 2023). </span><span>The metric is used to calculate both baseline and proposed post-development biodiversity units, and is </span>set to play an increasingly prominent role in nature conservation nationwide.<span> </span><span>The metric has so far </span>received limited scientific scrutiny.</p> <p><span>This dataset comprises a database of statutory biodiversity metric unit values for terrestrial habitat samples across England. For each habitat sample, we present </span><span>biodiversity units alongside five long-established single-attribute proxies for biodiversity (</span><span>species richness, individual abundance, number of threatened species, mean species range or population, mean species range or population change)</span><span>. </span><span>Data were compiled </span><span>for species from three taxa (vascular plants, butterflies, birds), from sites across England. The dataset includes 24 sites within </span>grassland, wetland, woodland and forest, sparsely vegetated land, cropland, heathland and shrub, i.e. <span>all terrestrial broad habitats except urban and individual trees. Species data were reused from long-term ecological change monitoring datasets</span> (mostly in the public domain), whilst biodiversity units were calculated following field visits. Fieldwork was carried out in April-October 2022 to calculate biodiversity units for the samples. <span>Sites were initially assessed using metric version 3.1, which was current at the time of survey, and were subsequently updated to the statutory metric for analysis using field notes and species data. </span>Species data <span>were derived from </span>24 <span>long-term ecological change monitoring</span> sites across the Environmental Change Network (ECN), Long Term Monitoring Network (LTMN) and Ecological Continuity Trust (ECT), collected between 2010 and 2020.</p>
Multi-antigen imaging data of skin tissue samples from cutaneous T cell lymphoma, atopic dermatitis, and psoriasis patients
<h1>Data</h1> <p>Multi-antigen imaging data for 69 skin tissue samples (21 cutaneous T cell lymphoma (CTCL), 23 atopic dermatitis (AD), and 25 pseudolymphoma (PSO)), obtained from 27 patients (8 CTCL, 7 AD, 12 PSO) treated at the University Hospital Erlangen. Each sample contains at least 36 protein channels, each of resolution 512 X 512 pixels. The data were generated using multi-epitope ligand cartography (MELC) (https://doi.org/10.1038/nbt1250, https://doi.org/10.1007/3-540-36459-5_8).</p> <h1>Metadata</h1> <p>For each sample, we information on sex, age, and condition of the corresponding patient. Samples and patients are numbered as S01 through S69 and P01 through P27, respectively.</p> <h1>Structure of the repository</h1> <ul> <li>The file <strong>metadata.csv</strong> contains the metadata.</li> <li>The archive <strong>data.zip</strong> contains one directory for each sample, named with the sample numbers S01 through S69.</li> <li>The directories for the individual samples contain images named<strong> <marker>-<flourescent protein>.tif</strong>, where <marker> is the name of the quantified protein marker and <flourescent protein> is the name of the flourescent protein that was used for image acquisition.</li> </ul> <p> </p> <p> </p>
Data S5. Sample cells
<p>This archive contains .kmz files for the 10 sample cells, each measuring 0.01x0.01 degrees, within the 13 macro-regions used in the Mangrove Threat Index validation process. Each .kmz file includes data on the 2010 mangrove extent, the 2010 anthropogenic activities, and the 2020 mangrove extent.</p>
Table presenting field data, isotopic data and 10Be exposure ages of cliffs and caprock-derived boulders sampled on the southern slope of the mesa of Szczeliniec Wielki (Central Sudetes, Stołowe Mountains, SW Poland)
<p><span>This research was funded by National Science Centre, Poland, research project no. 2020/39/D/ST10/00861.</span></p> <p><span>The data in the table presents the results of cosmogenic </span><sup><span>10</span></sup><span>Be exposure dating of cliffs and hillslope boulders resting on the southern slope of the sandstone mesa of Szczeliniec Wielki (Central Sudetes, Stołowe Mountains, SW Poland). The total of 20 samples was collected with growing distance from the present-day cliff lines. The samples were collected in October 2021, whereas their treatment and analyses were conducted in the following months at the University of Zurich (UZH) and ETH Zurich, Switzerland.</span></p> <p> </p> <p> </p>
Data for "Phase transition in Random Circuit Sampling"
<p><strong>Purpose</strong></p> <p>This dataset defines the Random Quantum Circuits (RQCs) used in our paper "Phase transition in Random Circuit Sampling" and lists the bitstrings observed in the experimental executions of the circuits on the Sycamore processor. See [1] for more details about the experiment. This data upload is modeled after that of [5].</p> <p><strong>Background</strong></p> <p><strong>Circuit parameters</strong></p> <p>RQCs posted here are uniquely identified<br>using the following parameters:</p> <ul> <li> `n`: number of qubits (69, 70),</li> <li> `m`: number of cycles (04, 06, 08, ..., 28, 30),</li> <li> `s`: seed for the pseudo-random number generator (000, 001, ..., 595),</li> <li> `patches`: the number of patches,</li> <li> `p`: sequence of coupler activation patterns (`ABCD`, `ABCDCDAB`),</li> <li> `num_sq`: the number of distinct single-qubit gates (3, 8),</li> <li> the date on which the data was collected, included in yymmdd format in the filename, and</li> <li> `phase_match`: included in the file name if phase matching was performed.</li> </ul> <p>See Figure S25 in [2] and Figure 3 of [1] for the coupler activation patterns and Figure 4 of [1] for illustrations of the patches. Also see code snippets below for visualizing the patches and activation patterns from the provided circuits.<br>When `num_sq` is 8, the single-qubit gates are chosen randomly from \(Z^p X^{1/2} Z^{-p}\), with \(p \in \{-1, -3/4, -1/2, -1/4, 0, 1/4, 1/2, 3/4 \}\), whereas when `num_sq` is 3, they are chosen randomly from among \(\sqrt X\), \(\sqrt Y\), and \(\sqrt W\). Phase matching is described in Appendix C.1 of [1]. Note that circuits which share the same seed `s` share the same initial gate sequence.</p> <p><strong>Content description</strong></p> <p>For each RQC there are four files in the dataset:<br> * original RQC specification in QSIM format, named `circuit_\*.qsim`,<br> * derived RQC specificaton as python code using cirq, named `circuit_\*.py`,<br> * derived RQC specification in QASM format, named `circuit_\*.qasm`,<br> * bitstrings observed in experiments, named `measurements_\*.txt`.</p> <p>The asterisk \* in the names above stands for a string specifying the parameters identifying a RQC. For example,<br>`circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.qasm` contains the definition of the 70-qubit, 24-cycle RQC with PRNG seed 0, 3 patches, a simplifiable sequence of coupler activation patterns (i.e. ABCD), and 8 distinct single-qubit gates, taken on October 14, 2022, with phase matching, in the QASM format.</p> <p>Files are grouped by parameters `n`, `m`, and `patches` and into compressed tarballs. For example, tarball `n69_m04_patches2.tar.gz` contains all RQCs and measurement files for circuits with 69 qubits, 4 cycles, and 2 patches.</p> <p><strong>Circuit file formats</strong></p> <p><strong>QSIM format</strong></p> <p>First line specifies the number of qubits n. Each subsequent line specifies a single gate and consists of moment number, gate name and one or two qubits as a number in 0..n-1 optionally followed by gate parameters.</p> <p>The circuits use the following gates:<br> * `x_1_2`: parameter-free, single-qubit pi/2 rotation around the X axis of the Bloch sphere, see equation (45) in section VII of [2],<br> * `y_1_2`: parameter-free, single-qubit pi/2 rotation around the Y axis of the Bloch sphere, see equation (46) in section VII of [2],<br> * `hz_1_2`: parameter-free, single-qubit pi/2 rotation around the X+Y axis of the Bloch sphere, see equation (47) in section VII of [2],<br> * `rz`: single-qubit rotation around the Z axis of the Bloch sphere through the angle specified in radians by the gate's sole parameter,<br> * `fsim`: two-qubit gate corresponding to the composition of the iSWAP and CPHASE gates and taking two parameters in radians: theta (the negative iSWAP angle) and phi (the CPHASE angle), see equation (48) in section VII of [2].</p> <p>Note that the two-qubit gates executed in our experiments on Sycamore belong to the five-parameter family of two-qubit gates that preserve the number of 0 and 1 states of the qubits. Each such gate can be decomposed into one fsim gate and four rz gates. Therefore, one cycle consisting of one application of<br>single-qubit gates and one application of two-qubit gates is represented in the file using four moments. The first moment contains `x_1_2`, `y_1_2` and `hz_1_2` gates. The other three moments use `rz` and `fsim` gates to describe the two-qubit gates used in the experiments. See section VII in [2] for more details about the Sycamore gates and their decomposition.</p> <p>Qubits are specified as numbers in 0..n-1 and hence do not directly indicate qubit location on the device.</p> <p><strong>Python/cirq format</strong></p> <p>Each python file defines two variables: QUBIT_ORDER and CIRCUIT. The former is a python list object containing `cirq.GridQubit` objects initialized with the<br>row and column of each qubit on the device. The latter is a `cirq.Circuit` object initialized with all gate operations contained in the circuit. The files have been tested using cirq version 1.2.0.dev20230613162638.</p> <p>The following code snippet illustrates how one can use cirq and our circuit definitions to compute output state amplitudes:</p> <pre><code>$ python -i circuit_n70_m24_s00_patches3_pABCD_num_sq8_221014_phase_match.py >>> cirq.final_wavefunction(CIRCUIT, qubit_order=QUBIT_ORDER) array([ 0.00263724+0.00337646j, 0.0009332 +0.00111853j, -0.0007809 +0.00386362j, ..., 0.00574739-0.00027827j, -0.00254766+0.00299345j, 0.00396056+0.00312335j], dtype=complex64)</code></pre> <p> </p> <p>See [3] for more details about cirq.</p> <p><strong>QASM format</strong></p> <p>Each QASM file has been generated using cirq and specifies the RQC decomposed into CNOT and single-qubit gates. The files have been generated using cirq.</p> <p>See [4] for more details about the format.</p> <p><strong>Measurements file format</strong></p> <p>Each line contains the bitstring obtained in a single execution of the RQC on Sycamore. The first, left-most position corresponds to the qubit 0 in QSIM format and the first qubit in the `QUBIT_ORDER` list in the python/cirq files.</p> <p><strong>To visualize the patches</strong></p> <p>After loading `CIRCUIT` from the appropriate `.py` file, the following code snippet can be used to visualize the patches:</p> <pre><code>import cirq import matplotlib.pyplot as plt pairs = set() for moment in CIRCUIT: for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add(tuple(sorted(q))) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax)</code></pre> <p> </p> <p><strong>Visualize the activation patterns</strong></p> <p>The activation pattern sequence can also be visualized in a similar manner. After loading `CIRCUIT` from the appropriate `.py` pyle, the following code snippet can be used to visualize the activation patterns:</p> <pre><code>import cirq import matplotlib.pyplot as plt def has_two_qubit_gates(moment): has = False for op in moment.operations: if len(op.qubits) > 1: has = True break return has def plot_pairs(moment): pairs = set() for op in moment.operations: q = op.qubits if len(q) > 1: assert len(q) == 2 pairs.add( tuple(sorted(q)) ) d = {p:1 for p in pairs} heatmap = cirq.TwoQubitInteractionHeatmap(d) _, ax = plt.subplots(figsize=(8, 8)) _ = heatmap.plot(ax) return ax moments = [_ for _ in CIRCUIT if has_two_qubit_gates(_)] moment_to_visualize = moments[0] # iterate through this manually plot_pairs(moment_to_visualize)</code></pre> <p> </p> <p><strong>Content listing</strong></p> <p>The dataset includes the following tarball files:</p> <pre><code>n69_m04_patches2.tar.gz (80 files) n69_m04_patches3.tar.gz (80 files) n69_m06_patches2.tar.gz (80 files) n69_m06_patches3.tar.gz (80 files) n69_m08_patches2.tar.gz (80 files) n69_m08_patches3.tar.gz (80 files) n69_m10_patches2.tar.gz (80 files) n69_m10_patches3.tar.gz (80 files) n69_m12_patches2.tar.gz (80 files) n69_m12_patches3.tar.gz (80 files) n69_m14_patches2.tar.gz (80 files) n69_m14_patches3.tar.gz (80 files) n69_m16_patches2.tar.gz (80 files) n69_m16_patches3.tar.gz (80 files) n69_m18_patches2.tar.gz (80 files) n69_m18_patches3.tar.gz (80 files) n69_m20_patches2.tar.gz (80 files) n69_m20_patches3.tar.gz (80 files) n69_m22_patches2.tar.gz (80 files) n69_m22_patches3.tar.gz (80 files) n69_m24_patches1.tar.gz (4 files) n69_m24_patches2.tar.gz (80 files) n69_m24_patches3.tar.gz (80 files) n69_m26_patches2.tar.gz (80 files) n69_m26_patches3.tar.gz (80 files) n69_m28_patches2.tar.gz (80 files) n69_m28_patches3.tar.gz (80 files) n69_m30_patches2.tar.gz (80 files) n69_m30_patches3.tar.gz (80 files) n70_m16_patches2.tar.gz (960 files) n70_m16_patches3.tar.gz (1040 files) n70_m16_patches9.tar.gz (160 files) n70_m18_patches2.tar.gz (960 files) n70_m18_patches3.tar.gz (1040 files) n70_m18_patches9.tar.gz (160 files) n70_m20_patches2.tar.gz (960 files) n70_m20_patches3.tar.gz (1040 files) n70_m20_patches9.tar.gz (160 files) n70_m22_patches2.tar.gz (960 files) n70_m22_patches3.tar.gz (1040 files) n70_m22_patches9.tar.gz (160 files) n70_m24_patches1.tar.gz (56 files) n70_m24_patches2.tar.gz (960 files) n70_m24_patches3.tar.gz (1040 files) n70_m24_patches9.tar.gz (160 files) n70_m26_patches1.tar.gz (4 files) n70_m26_patches2.tar.gz (880 files) n70_m26_patches3.tar.gz (960 files) n70_m26_patches9.tar.gz (160 files)<br>67-qubit data (described below)</code></pre> <p> </p> <p>Additionally, the file <code>Figures 2 and 3 data.zip</code> contains processed data (9 files in Pickle format) that are shown in Figures 2 and 3 of the paper.</p> <p>The 67-qubit data files, <code>n67_full.zip</code>, <code>n67_patches2.zip</code>, and <code>n67_patches3.zip</code> are formatted differently. The circuits are savd in .json format and can be loaded with `cirq.read_json()`. The data files are in .npy format and can be opened with numpy.load(). The number of cycles (`m` elsewhere) is indicated after `d` in the filename. The contents of these zip files can be previewed with the "preview" button.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>[1] Google AI Quantum and collaborators, "Phase transition in Random Circuit Sampling". <a href="https://arxiv.org/abs/2304.11119">arXiv:2304.11119</a></p> <p>[2] Google AI Quantum and collaborators, Supplementary information for “Quantum supremacy using a programmable superconducting processor”. <a href="https://arxiv.org/abs/1910.11333">arXiv:1910.11333</a></p> <p>[3] Cirq: A Python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits, <a href="https://github.com/quantumlib/Cirq">https://github.com/quantumlib/Cirq</a>.</p> <p>[4] Cross, Andrew W.; Bishop, Lev S.; Smolin, John A.; Gambetta, Jay M. "Open Quantum Assembly Language", <a href="https://arxiv.org/abs/1707.03429">arXiv:1707.03429</a></p> <p>[5] Martinis, John M. et al. (2022), "Quantum supremacy using a programmable superconducting processor", Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.k6t1rj8">https://doi.org/10.5061/dryad.k6t1rj8</a></p>
FY-3G PMR sample data
<p>The FY-3G PMR data used in Figure 1 in Liu et al. (2024).</p> <p>Paper: Liu, B., Li, H., Liu, L., Shang, J., Yin, H., & Kuo, K.-S. (2024). Sea surface and snowflakes as natural targets connecting FY-3G and GPM-CO dual-frequency radars. Geophysical Research Letters, 51, e2024GL110878. https://doi.org/10.1029/2024GL110878</p> <p>The FY-3G PMR data are available at: https://satellite.nsmc.org.cn/DataPortal/en/home/index.html</p> <p>Please email me if you encounter any difficulties accessing the data (202211030001@nuist.edu.cn).</p>
Sample data for analysis of FFPE sequencing data
Open the record for dataset details and reuse information.
Microsatellite data for Prosopis species sampled from different non-native populations in Kenya and Ethiopia
<p>Microsatellite data for seven loci and 711 individuals of <em>P. juliflora</em> and <em>P. pallida</em> sampled from non-native populations from Kenya and Ethiopia.</p>
Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection
<p>Reporting data from the Mosquito Alert citizen science system, active catch basin surveillance, and mosquito trap surveillance used in "Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection."</p> <p>The file named mosquito_alert_adult_bite_reports_Barcelona_2014_2023.Rds includes all adult mosquito and mosquito bite reports received from Barcelona Municipality from the start of the Mosqiuto Alert project in 2014 through the end of 2023. The file named mosquito_alert_validated_albopictus_reports_Barcelona_2014_23.Rds includes all expert-validated <em>Ae. albopictus </em>reports received from Barcelona Municipality during the same time period. The data is stored as RDS files and contain the following fields:</p> <ul> <li><strong>year </strong>- the year in which the report was made. Class = dbl.</li> <li><strong>date </strong>- the date om which the report was made. Class = date.</li> <li><strong>type </strong>- the report type, either adult mosquito ("adult") or mosquito breeding site ("site"). Class = chr.</li> <li><strong>lon</strong> - the longitude of the report location. Class = dbl.</li> <li><strong>lat</strong> - the latitude of the report location. Class = dbl.</li> <li><strong>validation_score</strong> - Entolab validation score. Either 1 (possible <em>Ae. albopictus</em>) or 2 (probable <em>Ae. albopictus</em>). This field is present only in the validated reports data. </li> </ul> <p>The file named active_catch_basin_drain_data.Rds includes information about all catch basin drains in Barcelona Municipality in which the Barcelona Public Health Agency (ASPB) detected mosquito activity as part of its continuous monitoring and control of mosquitoes from 2019 through 2023. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>any_reports </strong>- dummy variable indicating whether any Mosquito Alert adult mosquito or mosquito bite reports were sent through Mosquito Alert from within 200 m of the catch basin drain during the year in which the ASPB detected mosquito activity in hte catch basin drain. Class = lgl.</li> <li><strong>se_expected</strong> - sampling effort for the 0.025 degree lon/lat sampling cell in which the catch basin drain lies during the year in which the ASPB detected mosquito activity in the drain. This value is taken from the SE_expected variable in the sampling_effort_daily_cellres_025.csv.gz file available at https://zenodo.org/records/12602985. Sampling effort is estimated as the expected number of participants sending at least one report from the cell during the day in question given the the number of participants recorded in the cell that day and the amount of time elapsed since each one began participating in the project. Class = dbl.</li> <li><strong>p_singlehh</strong> - proportion of single-member households in the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_age </strong>- mean age of the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_rent_consumption_unit</strong> - mean income per consumption unit in the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>popd</strong> - population density of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>id_item </strong>- unique identifier given to the catch basin drain. Drain itentifiers appear multiple times in the data when the ASPB detected activity in the drain in multiple years. Class = dbl.</li> </ul> <p>The file named trap_data.Rds includes information on the adult mosquito trap surveillance analyzed in this article. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>females </strong>- number of Ae. albopictus females found in the trap. Class = dbl.</li> <li><strong>trap_name</strong> - unique identifier for the trap. Class = chr.</li> <li><strong>trapping_effort</strong> - number of days from when the trap was set to when it was checked. Class = dbl.</li> <li><strong>date</strong> - date on which the trap was checked. Class = date.</li> <li><strong>mean_tm30</strong> - mean temperature for the 30 days leading up to the date on which the trap was checked. Class = dbl.</li> <li><strong>mean_rent_consumption_unit </strong>- mean income per consumption unit for the census tract in which the trap was located. Class = dbl.</li> </ul>
Raman carbonaceous-material spectrum data used to estimate the peak temperatures of the samples
<p>Open files are Raman carbonaceous-material spectrum data obtained from the Ohyamamisaki conglomerate and the Higashigo Formation (eastern Shikoku) as well as the Nyunokawa conglomerate and the Ryujin Formation (central-western Kii Peninsula), Shimanto Accretionary Complex. The data can be analysed using the code proposed by Kaneki and Kouketsu (Island Arc, 2022, https://doi.org/10.1111/iar.12467) or PeakFit ver. 4.12. The data have been used in Shimura et al. (submitted to Tectonics).</p>
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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.