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3,134 results for “nuclear”
Nuclear Magnetic Resonance values for the Eptachori, Pentalofos and Tsotyli formations in West Macedonia
<p>The data comprises work under the Project Pilot Strategy GA No. 101022664, funded by the European Union. </p> <p>The work relates to rock samples collected in 2022 in West Macedonia, Greece. For full details, please refer to the following:</p> <ol> <li>Tsotyli formation: <a href="https://app.geosamples.org/sample/igsn/IE5770001">https://app.geosamples.org/sample/igsn/IE5770001</a> - <strong>WGS84 Lat : 40.3075, </strong><strong>WGS84 Long : 21.3354</strong></li> <li>Pentalofos formation: <a href="https://app.geosamples.org/sample/igsn/IE5770002">https://app.geosamples.org/sample/igsn/IE5770002</a> - <strong>WGS84 Lat : 40.1332,</strong> <strong>WGS84 Long : 21.1997</strong></li> <li>Eptachori formation: <a href="https://app.geosamples.org/sample/igsn/IE5770003">https://app.geosamples.org/sample/igsn/IE5770003</a> - <strong>WGS84 Lat : 40.1332, </strong><strong>WGS84 Long : 21.1997</strong></li> </ol> <p>The focus of the work is related to CO2 storage in appropriate saline aquifers in West Macedonia. The bulk samples were shipped to IFP Energies for porosity and permeability laboratory investigation conducted by Nuclear Magnetic Resonance techniques. </p> <p>Further to the raw data from the NMR, a depiction of the latter is provided in the corresponding figures</p> <p> </p>
Detailed abundances based on different nuclear physics for theoretical r-process scenarios
<p>This data set contains detailed abundances (at a time t=10^6 years after the event) for individual trajectories for seven different simulations of potential r-process sites, and based on nine different combinations of nuclear mass models and fission fragment distribution models. The data have been used and are discussed in Cote, Eichler, Yagüe, et al. (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) to determine the isotopic ratios of I129/Cm247 and compare them to meteoritic data.</p> <p>Furthermore, a code is included which samples a subset of trajectories reproducing the measured meteoritic I129/Cm247 abundance ratio of 438 +- 92. See the README file and the publication (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) for more details.</p>
Multiplexed DNA-FISH imaging dataset, drosophila embryos, nuclear cycles 11-14
<p>Multiplexed DNA-FISH imaging dataset from Drosophila embryos at nuclear cycles 11-14.</p> <p>Examples on how to load and use this dataset can be found at this <a href="https://github.com/NollmannLab/Goetz_etal">GitHub repository</a>.</p> <p><strong>Data processing details</strong></p> <p>Barcodes were segmented using a neural network (<a href="https://github.com/stardist/stardist"><em>stardist</em></a>) specifically trained for the detection of 3D diffraction limited spots produced by our microscope. To extract the position of the barcode with sub-pixel accuracy, a subsequent 3D Gaussian fit of the regions segmented by <em>stardist</em> was performed with Big-FISH (<a href="https://github.com/fish-quant/big-fish">https://github.com/fish-quant/big-fish</a>). Barcode localizations with intensities lower than 1.5 times that of the background were filtered out.</p> <p>Nuclei were segmented from projected DAPI images using <em><a href="https://github.com/stardist/stardist">stardist</a> </em>with a neural network trained for detection of nuclei from <em>Drosophila</em> embryos under our imaging conditions. Barcodes were then attributed to single nuclei by using the XY coordinates of the barcodes and the DAPI masks of the nuclei. Finally, pairwise distance matrices were calculated for each single nucleus.</p> <p><strong>Processed data in Figures</strong></p> <p>This new version of the dataset contains the raw data for each of the figures in the manuscript:</p> <p><strong>Associated publication</strong></p> <p><strong>Multiple parameters shape the 3D chromatin structure of single nuclei at the doc locus in </strong><em>Drosophila</em>.</p> <p>Markus Götz, Olivier Messina, Sergio Espinola, Jean-Bernard Fiche, Marcelo Nollmann</p> <p>Nature Communications (2022).</p>
Begomovirus DNA-B Movement Protein and Nuclear Shuttle Protein Ref-Seq Datasets
<p>Multiple Sequence Alignment of proteins encoded on begomovirus DNA-B (movement protein and nuclear shuttle protein; n=131). One isolate per species based on ICTV reference list.</p> <p> </p>
Dataset for: "Dynamical properties of solid and hydrated collagen: Insight from nuclear magnetic resonance relaxometry"
<p>The dataset contains a full set of 1H magnetization curves (1H magnetization versus time) for solid and hydrated collagen and collagen-based artificial tissues.</p> <p>DOI of article: <a href="https://doi.org/10.1063/5.0191409" target="_blank" rel="noopener">https://doi.org/10.1063/5.0191409</a></p> <p>This research was funded by the National Science Centre, Poland, Grant No. 2021/43/B/NZ5/01602.</p>
PARN and TOE1 constitute a 3′ end maturation module for nuclear non-coding RNAs
<p>HeLa cells were cultured in DMEM (Welgene) supplemented with 9% fetal bovine serum (Welgene). HeLa cells were transfected with 20 nM of siRNAs for four days using Lipofectamine 3000 (Thermo Fisher Scientific). Equal amounts of four different siRNAs were used for each knockdown. In the combinatorial knockdown, we mixed multiple siRNA pools to have a final concentration of 20 nM per siRNA pool. Total RNAs were extracted from siRNA-transfected HeLa cells using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol and treated with DNase I (Takara). mTAIL-seq libraries were prepared as previously described (Lim et al., 2016). Amplified cDNA libraries were sequenced on an Illumina MiSeq platform with 50% of the PhiX control library (Illumina).</p> <p>The uploaded file includes both intensity and sequence information for spike-ins and libraries used in the mTAIL-seq analysis. These data can be processed with Tailseeker 3.1.7 (Chang, 2017) according to the standard workflow of the software. The source codes and container images are available from Zenodo (https://zenodo.org/record/887547; doi:10.5281/zenodo.887546).</p>
Dataset from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters"
<p>This repository contains several data from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters".</p> <p> </p> <p><strong>BBH_mergers_cat_*.dat</strong> contains the data for the BBH merger population produced by the three-body simulations. These data can be used to reproduce figures 4,5, and 8 of the paper. The file is organized in columns as:</p> <ul> <li>ID of the simulation.</li> <li>outcome of the simulation (12, merger triggered by a flyby event, 13 and 23 merger triggered after an exchange event in which the secondary (primary) BH is replaced by the intruder, 123 second generation BBH merger.</li> <li>mass of the primary BH in solar masses</li> <li>mass of the secondary BH in solar masses</li> <li>Chirp mass of the system in solar masses</li> <li>coalescence time since the beginning of the simulation in year (note that all the simulation with tcoal<1e5 yr have merged during the direct N-body simulation, while all the mergers that take place after this value are evolved with the equations by Peters 1964)</li> <li>eccentricity of the binary at 10 Hz in the detector frame</li> <li>tilt angle in radiant, defined as the angle between the orbital plane of the initial binary at the beginning of the simulation and the orbital plane of the final binary at the end of the simulation.</li> </ul> <p>The files named <strong>data_*.txt</strong> contains the masses, the position and the velocities at each timestep for the three simulations showed in fig.1 in the paper. The data are referred to the center-of-mass of the three-body system. The file is organized as follows:</p> <ul> <li>The first line of the file reports the masses in solar masses of the three BHs.</li> <li>Column 0 reports the time in yr</li> <li>Colum 1-3 report the x,y,z position for the m1 BH in parsec</li> <li>Colum 4-6 report the x,y,z position for the m2 BH in parsec</li> <li>Colum 7-9 report the x,y,z position for the m3 BH in parsec</li> <li>Colum 10-12 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 13-15 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 16-18 report the x,y,z components of the velocities of the m1 BH in km/s</li> </ul> <p>Finally, <strong>outcomes_*.dat</strong> contains two columns:</p> <ul> <li>Column 0 reports the ID of the simulation</li> <li>Column 1 reports the outcome of the simulation as: 12 flyby (or merger after a flyby), 13 and 23 exchange (or merger after an exchange) in which the secondary (primary) BH is replaced by the intruder, 0 in the system is ionized in three single BHs, 3 if the system is still interacting at 1Myr, i.e. when we stop our simulation.</li> </ul> <p>This file might be useful to train a machine-lerning classificator, and can be used to reproduce Fig. 2 of the paper.</p> <p> </p> <p><strong>Contacts:</strong></p> <p>Marco Dall'Amico</p> <p>marco.dallamico@phd.studenti.unipd.it</p> <p>marco.dallamico@pd.infn.it</p>
Dataset for Precursor Nuclearity and Ligand Effects in Atomically-Dispersed Heterogeneous Iron Catalysts for Alkyne Semi-Hydrogenation
<p>This dataset complements the publication entitled "Precursor Nuclearity and Ligand Effects in Atomically-Dispersed Heterogeneous Iron Catalysts for Alkyne Semi-Hydrogenation" by Dario Faust Akl, Andrea Ruiz-Ferrando, Dr. Edvin Fako, Dr. Roland Hauert, Dr. Olga Safonova, Dr. Sharon Mitchell, Prof. Núria López, Prof. Javier Pérez-Ramírez. Please refer to the Readme.txt file for information about the file structure and content.<br> </p>
NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"
<p>Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion".</p> <p>The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part.</p> <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> <p>The codes used to analyze the data are available at in a next upload.</p> <p>Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.</p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: EOS library
<p>This repository includes a library of equations of state (EOS) and stellar models presented in the publications Weih et al. (2019) (see also the related identifier) and Most et al. (2018). The library includes ~ 3 Million physically plausible EOSs that fulfill a number of astrophysical and nuclear constraints. See the README for more information. </p>
Phylogenomics of Gesneriaceae using targeted capture of nuclear genes
<p>Gesneriaceae (ca. 3400 species) is a pantropical plant family with a wide range of growth form and floral morphology that are associated with repeated adaptations to different environments and pollinators. Although Gesneriaceae systematics has been largely improved by the use of Sanger sequencing data, our understanding of the evolutionary history of the group is still far from complete due to the limited number of informative characters provided by this type of data. To overcome this limitation, we developed here a Gesneriaceae-specific gene capture kit targeting 830 single-copy loci (776,754 bp in total), including 279 genes from the Universal Angiosperm-353 kit. With an average of 557,600 reads and 87.8% gene recovery, our target capture was successful across the family Gesneriaceae and also in other families of Lamiales. From our bait set, we selected the most informative 418 loci to resolve phylogenetic relationships across the entire Gesneriaceae family using maximum likelihood and coalescent-based methods. Upon testing the phylogenetic performance of our baits on 78 taxa representing 20 out of 24 subtribes within the family, we showed that our data provided high support for the phylogenetic relationships among the major lineages, and were able to provide high resolution within more recent radiations. Overall, the molecular resources we developed here open new perspectives for the study of Gesneriaceae phylogeny at different taxonomical levels and the identification of the factors underlying the diversification of this plant group. </p>
Modeled tritium in precipitation from Fukushima Daiichi Nuclear Power Plant accident simulations with MIROC5-iso
<p>This data set contains modeled tritium in precipitation values from different simulations of Fukushima Daiichi Nuclear Power Plant (FDNPP) accident produced with MIROC5-iso. The simulations are for the period 2011-20121 and were with different anthropogenic tritium source functions. A complete description can be found in Cauquoin, A., Gusyev, M., Bong, H., Okazaki, A., and Yoshimura, K.: Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times, <em>Environ. Sci. Pollut. Res.</em>, <a href="https://doi.org/10.1007/s11356-025-35919-1" target="_blank" rel="noopener">https://doi.org/10.1007/s11356-025-35919-1</a>, 2025. </p> <p>The simulations are named fukushima_accident_{jra55, era5}_total_gas_{div100, div200, div500, div1000}, with {jra55, era5} describing a nudging to JRA-55 or ERA5 reanalyses, and with {div100, div200, div500, div1000} describing the anthropogenic tritium input function used in DatasetS1_table_tritium_release_atm_fukushima_input.csv.</p> <p>The modeled values of tritium in Hiso river water, Minamisoma spring and artesian groundwater, calculated using MIROC5-iso tritium in monthly precipitation in Fukushima, scaled Tokyo GNIP data, and tritium measurements in preciptation at Fukushima as input of the TracerLPM model, are included too. </p> <p>The model data can be downloaded as netcdf, csv or xlsx files:</p> <ul> <li>*_daymean.prcpTU.nc: daily mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_monmean.prcpTU.nc: monthly mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_daymean.prcp.nc: daily precipitation over the period 2011-2021, expressed in mm/day;</li> <li>*_monmean.prcp.nc: monthly precipitation over the period 2011-2021, expressed in mm/month;</li> <li>*_prcp_daymean.remapnn.csv: daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in mm/day;</li> <li>*_prcp_monmean.remapnn.csv: montly mean precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in mm/month;</li> <li>*_prcpTU_daymean.remapnn.csv: tritium in daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in TU;</li> <li>*_prcpTU_monmean.remapnn.csv: tritium in montly precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in TU;</li> <li>DatasetS1_table_tritium_release_atm_fukushima_input.csv: Table of anthropogenic tritium daily release, based on reconstructed iodine-131 total gas emissions from <a href="https://doi.org/10.5194/acp-15-1029-2015" target="_blank" rel="noopener">Katata et al. (2015)</a>, used as inputs for MIROC5-iso.</li> <li>TracerLPM_fukushima_with_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to JRA-55 was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_without_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation ctrl nudged to JRA-55 (without FDNPP peak) was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_with_peak_era5.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to ERA5 was used for constructing Cin(t).</li> </ul>
Argument Aspect Corpus - Nuclear Energy
<p>The Argument Aspect Corpus–Nuclear Energy (AAC-NE) contains English-language sentences with aspect annotations describing the content of arguments on the topic of nuclear energy.</p> <p>It was introduced in this paper:</p> <blockquote> <p>Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., & Torge, S. (2022). Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association (ELRA).</p> </blockquote> <p>The AAC-NE corpus is based on a subset of all argumentative sentences contained in the UKP SAM dataset [1] for which a majority vote of three annotators could be achieved during the annotation of the main argument aspect of each sentence.</p> <p>The CSV files contain one of nine aspect labels per argumentative sentence split into training, dev, and test set.</p> <table> <thead> <tr> <th><strong>aspect</strong></th> <th><strong>train</strong></th> <th><strong>dev</strong></th> <th><strong>test</strong></th> <th><strong>Sum</strong></th> <th><strong>Kripp. Alpha</strong></th> </tr> </thead> <tbody> <tr> <td>alternatives</td> <td>100</td> <td>16</td> <td>21</td> <td>137</td> <td>0.69</td> </tr> <tr> <td>costs</td> <td>98</td> <td>17</td> <td>29</td> <td>144</td> <td>0.72</td> </tr> <tr> <td>environment</td> <td>209</td> <td>27</td> <td>64</td> <td>300</td> <td>0.74</td> </tr> <tr> <td>innovation</td> <td>33</td> <td>2</td> <td>8</td> <td>43</td> <td>0.38</td> </tr> <tr> <td>reactor safety</td> <td>112</td> <td>17</td> <td>43</td> <td>172</td> <td>0.59</td> </tr> <tr> <td>reliability</td> <td>47</td> <td>5</td> <td>10</td> <td>62</td> <td>0.36</td> </tr> <tr> <td>waste</td> <td>87</td> <td>5</td> <td>26</td> <td>118</td> <td>0.80</td> </tr> <tr> <td>weapons</td> <td>52</td> <td>11</td> <td>15</td> <td>78</td> <td>0.77</td> </tr> <tr> <td>other</td> <td>120</td> <td>23</td> <td>29</td> <td>172</td> <td>0.49</td> </tr> <tr> <td><strong>all</strong></td> <td><strong>858</strong></td> <td><strong>123</strong></td> <td><strong>245</strong></td> <td><strong>1226</strong></td> <td><strong>0.62</strong></td> </tr> <tr> <td>pro</td> <td> </td> <td> </td> <td> </td> <td>706</td> <td> </td> </tr> <tr> <td>cons</td> <td> </td> <td> </td> <td> </td> <td>520</td> <td> </td> </tr> </tbody> </table> <p>Additionally, it contains 2000 unlabeled sentences with presumably argumentative content sampled from the newspaper “The Guardian”.</p> <p>[1] Stab, C., Miller, T., Schiller, B., Rai, P., & Gurevych, I. Cross-topic Argument Mining from Heterogeneous Sources. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3664–3674). Association for Computational Linguistics. <a href="https://doi.org/10.18653/v1/D18-1402">https://doi.org/10.18653/v1/D18-1402 </a></p> <p> </p>
Nuclear genotypes from Coryphaena hippurus
<p>This dataset comprises genotypes from nuclear microsatellites of Coryphaena hippurus samples. </p> <p>We amplified 14 nuclear microsatellites; five of which were designed by Chapmann (pers. comm) and obtained from GenBank accession numbers: AY135025-AY132028, and AY189832. The rest nine loci were designed by Bayona-Vásquez et al. (2015). Microsatellites loci were amplified from 311 tissue samples from Coryphaena hippurus commonly know as mahi-mahi. Samples were obtained across nine localities within the Tropical Eastern Pacific: Bahía Magdalena (BM); Punta Lobos (PL); Cabo San Lucas (CSL); Guaymas (GU), Mazatlan (MZ); Chiapas (PM); Ecuador (EC); Perú (PE); and ocean sample (OC). Genotypes were recorded in three and two digits. Missing data coded as -9.</p> <p>Geographic coordinates of sample localities:</p> <p>Bahía Magdalena (BM): 24.579118, -111.998283<br> Punta lobos (PL): 23.413503, -110.234702<br> Cabo San Lucas (CSL) : 22.869653, -109.898667<br> Guaymas, Sonora (GU): 27.825287, -110.933325<br> Oceanic sample (OC): 11.886755, -122.880087<br> Mazatlán, Sinaloa (MZ): 23.192460, -106.471719<br> Puerto Madero, Chiapas (PM):14.664185, -92.482619<br> Ecuador (EC):-1.484832, -81.457839<br> Perú (PE): -12.184145, -77.250531</p>
The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.
<p>This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>.</p> <p>The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. </p> <p>Data files were prepared by agnieszka.gierej@vub.be</p>
Data from "Source characterization of the declared North Korean Nuclear Tests from regional distance coda wave spectral ratios"
<p>Results of the coda spectral ratio analysis presented in Delbridge et al. (2022).</p> <p>network_average_ratios.csv - a csv file which contains each of the network average ratios calculated from all channels and stations for each event pair and component.</p>
Data of publication All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles
<p>Data corresponding to the figures of the publication "All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles" by D. Serrano et al. (https://www.nature.com/articles/s41467-018-04509-w). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
MetaboScope: A statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies.
<p>MetaboScope is purposefully built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The code is open source (DOI: https://www.cheminfo.org/flavor/metabolomics/index.html) and can be readily installed locally. We provide module notes and video tutorials, in addition to clinical spectral datasets for modelling purposes.</p> <p>View data:</p> <p><a title="nmrium.org" href="https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content" target="_blank" rel="noopener">https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content</a></p>
Plasmid Maps for a Nuclear Transformation Vector in Chlamydomonas reinhardtii for the Expression and Secretion of the Plastic-Degrading Enzyme (PHL7)
<p><strong>pJP32PHL7 Vector:</strong></p> <ul> <li> <p><strong>Size:</strong> 5692 bp</p> </li> <li> <p><strong>Key Features:</strong></p> <ul> <li><strong>HSP70 Promoter:</strong> A heat shock protein promoter fused with the <em>rbcS2</em> promoter to drive expression of downstream genes.</li> <li><strong>Ble Resistance Gene:</strong> Confers resistance to bleomycin, useful for selection in <em>Chlamydomonas reinhardtii</em>.</li> <li><strong>PHL7 Gene:</strong> Encodes the plastic-degrading enzyme PHL7, inserted downstream of the <em>F2A</em> site for expression in the host.</li> <li><strong>Intron Sequences:</strong> Contains multiple <em>rbcS2</em> introns for enhancing expression in <em>Chlamydomonas</em>.</li> <li><strong>Selectable Marker (AmpR):</strong> Confers ampicillin resistance for selection in <em>E. coli</em>.</li> <li><strong>Replication Origin:</strong> Includes <em>ori</em> and <em>F1 ori</em> for replication in <em>E. coli</em>.</li> </ul> <p> </p> </li> <li> <p><strong>Applications:</strong> This vector is designed for nuclear transformation in <em>Chlamydomonas reinhardtii</em>, enabling the expression and secretion of the plastic-degrading enzyme (PHL7) under the control of a hybrid <em>HSP70</em>rbcS2 promoter.</p> </li> </ul> <p><strong>pJP32PHL7dg Vector:</strong></p> <ul> <li> <p><strong>Size:</strong> 5692 bp</p> </li> <li> <p><strong>Key Features:</strong></p> <ul> <li><strong>HSP70 Promoter:</strong> Retains the HSP70 and <em>rbcS2</em> fusion promoter for gene expression.</li> <li><strong>LacZ Alpha Fragment:</strong> Includes a LacZ alpha fragment for blue/white screening.</li> <li><strong>PHL7 Gene:</strong> Encodes the plastic-degrading enzyme PHL7, linked downstream of the <em>F2A</em> site, allowing for expression in the host.</li> <li><strong>Ble Resistance Gene:</strong> Also confers bleomycin resistance for selection in <em>Chlamydomonas</em>.</li> <li><strong>Selectable Marker (AmpR):</strong> Confers ampicillin resistance for selection in <em>E. coli</em>.</li> <li><strong>Intron Sequences:</strong> Contains <em>rbcS2</em> introns for optimizing gene expression in the host organism.</li> </ul> <p> </p> </li> <li> <p><strong>Applications:</strong> The pJP32PHL7dg vector is similarly designed for nuclear transformation in <em>Chlamydomonas reinhardtii.</em> It also facilitates the expression and secretion of the plastic-degrading enzyme PHL7, driven by the hybrid <em>HSP70</em>rbcS2 promoter, but without glycosilation sites.</p> </li> </ul>
Nuclear Magnetic resonance Dataset of 2D spectra of S100B and Tau to study their protein-protein interaction
<p>Nuclear Magnetic resonance dataset of 2D spectra corresponding to raw data of research published in Nature Communication in a communication entitled "Dynamic interactions and Ca2+ 1 -binding modulate the holdase-type chaperone activity of S100B preventing tau aggregation and seeding" by Moreira G. et al.</p> <p>Dataset corresponds to</p> <p>raw data files in Bruker format of NMR 2D spectra (ser), associated with files of acquisition parameters and processing parameters (pdata),</p> <p>files in .ucsf format that can be read with NMRFAM-Sparky (free download) of 2D spectra (in sub-directory pdata/1)</p> <p>files of chemical shift value lists that can be read as text files or in NMRFAM sparky together with the corresponding ucsf files.</p> <p>physico-chemical conditions are found in title in pdata\1</p> <p>Data were acquired on a Bruker 900-MHz spectrometer equipped with a triple-resonance cryogenic probe (Bruker, Karlsruhe, Germany)</p>
ScienceDex guides
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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.