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.
550
datasets available to search
ShareScore release 0.7.1
Dataset results
550 results for “copper”
Tagging and tracking information for radiotagged Chinook Salmon in the Copper River, Alaska, 2019 and 2020.
<p>This is a 2 year data set that summarizes the information collected for a study funded by the Alaska Sustainable Salmon Fund (AKSSF), project number 52004. Data was collected on Chinook Salmon in the Copper River by the Alaska Department of Fish and Game, Sport Fish Division and the Native Village of Eyak. The data set includes the following information on Chinook Salmon: tagging date, radio tag frequency and code, length of fish, age of fish and then the date each fish passed a series of fixed tracking stations along the Copper River, expressed as Julian dates. Below is the citation for the study plan which details the methodology including tagging location and placement of the fixed-tracking stations.</p> <p>Schwanke, C. J. 2019. Run timing and spawning distribution of Copper River Chinook salmon. Alaska Department<br> of Fish and Game, Regional Operational Plan ROP.SF.3F.2019.04, Fairbanks.</p> <p>http://www.adfg.alaska.gov/FedAidPDFs/ROP.SF.3F.2019.04.pdf</p>
Simulated industrial CT dataset for deep learning with dual-energy tomograms and ground truth material maps for copper and iron
<p>We use this dataset for training and evaluation of a deep learning model to discriminate multi-material systems with X-ray CT.</p> <p>The dataset consists of:</p> <ul> <li>inputs: dual-energy tomograms as binary files without a header (tensor <strong>shape for numpy: 2x128x128 @float32</strong>) <ul> <li>simulated spectra are 250kVp and 450kVp both prefiltered using 2mmCuSn</li> </ul> </li> <li>outputs: the material maps a.k.a. ground truths for the training (same shape as inputs) <ul> <li>sampled with a delaunay algorithm and randomly filled with iron and copper fractions</li> </ul> </li> </ul> <p>The <strong>dataset is normalized to [0, 1]</strong>, so you have to multiply by the mass densities of copper and iron to obtain effective fractions in g/cm^3.</p>
SO2 and copper tolerance exhibit an evolutionary trade-off in Saccharomyces cerevisiae
<p>Copper tolerance and sulfite tolerance are two well-studied phenotypic traits of <em>Saccharomyces cerevisiae</em>. The genetic bases of these traits are derived from allelic expansion at the CUP1 locus and reciprocal translocation at the SSU1 locus, respectively. Previous work identified a negative association between sulfite and copper tolerance in <em>S. cerevisiae</em> wine yeasts. Here we probe the relationship between sulfite and copper tolerance and show that an increase in <em>CUP1</em> copy number does not impart copper tolerance in all <em>S. cerevisiae</em> wine yeast. Bulk-segregant QTL analysis was used to identify variance at <em>SSU1</em> as a causative factor in copper sensitivity, which was verified by reciprocal hemizygosity analysis in a strain carrying 20 copies of <em>CUP1</em>. Transcriptional and proteomic analysis demonstrated that <em>SSU1</em> over-expression did not suppress <em>CUP1</em> transcription or constrain protein production but suggested that <em>SSU1</em> overexpression induced sulfur limitation during exposure to copper. Finally, an <em>SSU1</em> over-expressing strain exhibited increased sensitivity to moderately elevated copper concentrations in sulfur-limited medium, demonstrating that <em>SSU1</em> over-expression burdens the sulfate assimilation pathway. Over-expression of MET 3/14/16, genes upstream of H<sub>2</sub>S production in the sulfate assimilation pathway increased the production of SO<sub>2</sub> and H<sub>2</sub>S but did not improve copper sensitivity in an <em>SSU1</em> overexpressing background. We conclude that copper and sulfite tolerance are conditional traits in <em>S. cerevisiae</em> and provide evidence of the metabolic basis for their mutual exclusivity. These findings suggest an evolutionary basis for the extreme amplification of <em>CUP1</em> observed in some yeasts.</p>
Chlorine-Promoted Copper Catalysts for CO2 Electroreduction into Highly Reduced Products
<p>Datasets supporting the publication 'Chlorine-Promoted Copper Catalysts for CO<sub>2</sub> Electroreduction into Highly Reduced Products': catalyst evaluation data (Excel), XRD (2 column CSV), XPS (Excel), SEM images (TIFF)</p>
Dataset belonging to the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper"
<p>This repository contains the raw data of the experimental (S)TEM imaging and the data corresponding to the simulations and theoretical calculations of the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper",. A pre-print version of the paper is available on arXiv: <a href="http://doi.org/10.48550/arXiv.2212.01180">http://doi.org/10.48550/arXiv.2212.01180</a></p> <p>See the file README.md for a detailed description.</p>
Copper Carbazole Diphosphonate - 2
<p><em><strong>CAU-37-act</strong></em></p> <p>The following submission contains the data collection and processing for the sample CAU-37-act in the framework of the publication: <strong>Synthesis and Structure Evolution in Metal Carbazole Diphosphonates Followed by Electron Diffraction</strong>. Felix Steinke, Laura Gemmrich Hernandéz, Stephen J. I. Shearan, Maxi Pohlmann, Marco Taddei, Ute Kolb, and Norbert Stock. Inorganic Chemistry 2023 62 (1), 35-42.</p> <p>Precession Electron Diffraction (PED) was used to collect the dataset on the target crystal. The dataset was processed with PETS2 and eADT software. The table below summarizes the data collection parameters for the dataset.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p> </p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p> </p> </td> <td> <p>ESR8</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p> </p> </td> <td> <p>CAU-37</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p> </p> </td> <td> <p>CAU-37-act</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p> </p> </td> <td> <p>CuDPC_Cry6</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p> </p> </td> <td> <p>FEI TECNAI F30 STWIN</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p> </p> </td> <td> <p>FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p> </p> </td> <td> <p>300 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p> </p> </td> <td> <p>0.0197 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p> </p> </td> <td> <p>Nanodiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p> </p> </td> <td> <p>200nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p> </p> </td> <td> <p>Semi-parallel beam</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p> </p> </td> <td> <p>US4000 - CCD camera GATAN (16-bit) (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p> </p> </td> <td> <p>2048 x 2048</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p> </p> </td> <td> <p>15 µm x 15 µm</p> </td> </tr> <tr> <td> <p>Camera Length / Effective Camera Length</p> </td> <td> <p> </p> </td> <td> <p>1500 mm / 1500 mm</p> </td> </tr> <tr> <td> <p>Calibration constant (not corrected for Effective Camera length)</p> </td> <td> <p> </p> </td> <td> <p>0.00074 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> Hardware Binning</p> </td> <td> <p> </p> </td> <td> <p>2 </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p> </p> </td> <td> <p>CAU-37-act</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p> </p> </td> <td> <p>[Cu<sub>2</sub>(H<sub>2</sub>O)<sub>2</sub>(L)]∙2H<sub>2</sub>O]</p> <p>3,6-diphosphono-9H-carbazole (H<sub>4</sub>L)</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p> </p> </td> <td> <p>Synthesized</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p> </p> </td> <td> <p>Grinded in an Agatha mortar and suspended in 1ml of EtOH. 4 µL of the suspension were dropped with a pipette on the carbon side of a carbon-coated copper grid (300 mesh).</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p> </p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p> </p> </td> <td> <p>Precession</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p> </p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p> </p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p> </p> </td> <td> <p>121</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p> </p> </td> <td> <p>-60° to +60°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p> </p> </td> <td> <p>1°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p> </p> </td> <td> <p>4 s</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p> </p> </td> <td> <p>Gatan Digital Micrograph software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p> </p> </td> <td> <p>PETS2 and eADT</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p> </p> </td> <td> <p>Laura Gemmrich Hernández (ESR8)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> <td> <p> CAU-37-as & CAU-57</p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> <td> <p> https://doi.org/10.1021/acs.inorgchem.2c02599</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p> </p> </td> <td> <p>img: Folder containing images of the diffraction pattern from each frame.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p> </p> </td> <td> <p>tiff_16bit_unsigned</p> </td> </tr> <tr> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Copper Carbazole Diphosphonate
<p><em><strong>CAU-37-as</strong></em></p> <p>The following submission contains the data collection and processing for the sample CAU-37-as in the framework of the publication: <strong>Synthesis and Structure Evolution in Metal Carbazole Diphosphonates Followed by Electron Diffraction</strong>. Felix Steinke, Laura Gemmrich Hernandéz, Stephen J. I. Shearan, Maxi Pohlmann, Marco Taddei, Ute Kolb, and Norbert Stock. Inorganic Chemistry 2023 62 (1), 35-42.</p> <p>Precession Electron Diffraction (PED) was used to collect the dataset on the target crystal. The dataset was processed with PETS2 and eADT software. The table below summarizes the data collection parameters for the dataset.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p> </p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p> </p> </td> <td> <p>ESR8</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p> </p> </td> <td> <p>CAU-37</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p> </p> </td> <td> <p>CAU-37-as</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p> </p> </td> <td> <p>CuDPC_Cry5</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p> </p> </td> <td> <p>FEI TECNAI F30 STWIN</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p> </p> </td> <td> <p>FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p> </p> </td> <td> <p>300 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p> </p> </td> <td> <p>0.0197 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p> </p> </td> <td> <p>Nanodiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p> </p> </td> <td> <p>200nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p> </p> </td> <td> <p>Semi-parallel beam</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p> </p> </td> <td> <p>US4000 - CCD camera GATAN (16-bit) (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p> </p> </td> <td> <p>2048 x 2048</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p> </p> </td> <td> <p>15 µm x 15 µm</p> </td> </tr> <tr> <td> <p>Camera Length / Effective Camera Length</p> </td> <td> <p> </p> </td> <td> <p>1500 mm / 1500 mm</p> </td> </tr> <tr> <td> <p>Calibration constant (not corrected for Effective Camera length)</p> </td> <td> <p> </p> </td> <td> <p>0.00074 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> Hardware Binning</p> </td> <td> <p> </p> </td> <td> <p>2 </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p> </p> </td> <td> <p>CAU-37-as</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p> </p> </td> <td> <p>[Cu<sub>2</sub>(H<sub>2</sub>O)<sub>2</sub>(L)]∙2H<sub>2</sub>O]</p> <p>3,6-diphosphono-9H-carbazole (H<sub>4</sub>L)</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p> </p> </td> <td> <p>Synthesized</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p> </p> </td> <td> <p>Grinded in an Agatha mortar and suspended in 1ml of EtOH. 4 µL of the suspension were dropped with a pipette on the carbon side of a carbon-coated copper grid (300 mesh).</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p> </p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p> </p> </td> <td> <p>Precession</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p> </p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p> </p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p> </p> </td> <td> <p>121</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p> </p> </td> <td> <p>-60° to +60°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p> </p> </td> <td> <p>1°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p> </p> </td> <td> <p>4 s</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p> </p> </td> <td> <p>Gatan Digital Micrograph software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p> </p> </td> <td> <p>PETS2 and eADT</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p> </p> </td> <td> <p>Laura Gemmrich Hernández (ESR8)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> <td> <p>CAU-37-act & CAU-57</p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> <td> <p> https://doi.org/10.1021/acs.inorgchem.2c02599</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p> </p> </td> <td> <p>img: Folder containing images of the diffraction pattern from each frame.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p> </p> </td> <td> <p>tiff_16bit_unsigned</p> </td> </tr> </tbody> </table> <p> </p>
Copper occurrence data related to EFSA opinion on copper 2023
<p>The file contains the raw occurrence dataset on copper in food as extracted from EFSA DWH on 10th of March 2021 and presented in the EFSA opinion on "Re‐evaluation of the existing health‐based guidance values for copper and exposure assessment from all sources" available at https://doi.org/10.2903/j.efsa.2023.7728. The data is provided in csv format. This dataset is compliant with EFSA SSD2 data model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields).</p> <p>The link to the catalogues of controlled terminologies for the updated textual description of fields values can be found under "Related identifiers”.</p>
Gate-Tunable Spin Hall Effect in an All-Light-Element Heterostructure: Graphene with Copper Oxide
<p>Graphene is a light material for long-distance spin transport due to its low spin–orbit coupling, which at the same time is the main drawback for exhibiting a sizable spin Hall effect. Decoration by light atoms has been predicted to enhance the spin Hall angle in graphene while retaining a long spin diffusion length. Here, we combine a light metal oxide (oxidized Cu) with graphene to induce the spin Hall effect. Its efficiency, given by the product of the spin Hall angle and the spin diffusion length, can be tuned with the Fermi level position, exhibiting a maximum (1.8 ± 0.6 nm at 100 K) around the charge neutrality point. This all-light-element heterostructure shows a larger efficiency than conventional spin Hall materials. The gate-tunable spin Hall effect is observed up to room temperature. Our experimental demonstration provides an efficient spin-to-charge conversion system free from heavy metals and compatible with large-scale fabrication.</p>
Data from: "Fragmentation and detachment of hot copper and silver dimer anions: a comparison"
<p>The files found here are text files with data related to the article: "Fragmentation and detachment of hot copper and silver dimer anions: a comparison" published in Phys Rev A (DOI: 10.1103/PhysRevA.107.062824). The files contain the information necessary to reproduce all figures containing data from the experiment or from the described calculations. Each file contains a header explaining the data sets which follow.</p>
Molecular simulations and machine learning potentials for graphene on liquid copper
<p>Dataset for the paper:</p> <p>Gao, H. et al. Graphene at Liquid Copper Catalysts: Atomic-Scale Agreement of Experimental and First-Principles Adsorption Height. Advanced Science 9, 2204684 (2022). (DOI: 10.1002/advs.202204684)</p> <p> </p> <p>zenodo/dataset/: Training and test sets for MTP</p> <p>zenodo/md/: Initial atomic models of the Gr-Cu interface, input files and resulting trajectories of MD simulations</p> <p>zenodo/potentials/: Trained MTP potential files</p> <p> </p>
SO2 and copper tolerance exhibit an evolutionary trade-off in Saccharomyces cerevisiae
Open the record for dataset details and reuse information.
Raw data for Article "Copper-Catalyzed Oxyvinylation of Diazo Compounds"
<p>Raw NMR, MS and IR data for the related publication in Organic Letters: <a href="http://dx.doi.org/10.1021/acs.orglett.0c01150">http://dx.doi.org/10.1021/acs.orglett.0c01150</a></p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
On the anomalous shapes of native copper crystals from the Michigan Copper Country
<p>Raw NanoSIMS image data files and metadata files with analytical conditions for each analysis. Data associated with manuscript "On the anomalous shapes of native copper crystals from the Michigan Copper Country" by Boulliard, Aléon and Gaillou, accepted in European Journal of Mineralogy, 2021.</p>
Seal. Rectangular metal seal cast in a copper alloy; one-line inscription
<p>Seal. Rectangular metal seal cast in a copper alloy; one-line inscription. British Museum 1892,1103.95.</p> <p> </p>
Copper And Wood Ear Disc (71a983)
**Copper-and-wood ear disc** Location: Town Creek site (31Mg2-3), Montgomery County, North Carolina. Period: Mississippian (AD 1150-1400). Material: copper and wood. Dimensions: length, 53.7 mm; width, 47.6 mm; thickness, 5.5 mm. Notes: Catalog no. 71a983, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
Guerrero Copper Sheet in Bedrock No.3 (2019)
Site 2-04-8MO02343: Pieces of crumpled copper sheeting are driven into a hole in the limestone bedrock, as well as fused to the surface of it. The metal sheeting once covered the lower hull of the ship to prevent shipworm infestation and fouling of the bottom. Model created June, 2019. Scale = 5 centimeters. Source: Objaverse 1.0 / Sketchfab
Guerrero Copper Sheet on Bedrock No.1 (2019)
Site 2-04-8MO02343: A piece of copper sheet is found fused to the sea bottom at the site of a shipwreck believed to be the Havana-based pirate slave ship *Guerrero*, sunk near Key Largo in 1827. This piece is likely a remnant of copper sheathing used to protect the lower part of the ship from shipworms and fouling. A copper-alloy tack sits on the piece, evidence of the type of fastener used to attach the copper to the ship's hull. This same feature was rendered earlier with images taken in 2012 (see https://skfb.ly/6pwSy), but this newer render is much clearer. Model created June, 2019. Scale = 5 centimeters. Source: Objaverse 1.0 / Sketchfab
Stickley copper candlestick
Candlestick attributed to Gustav Stickley Gustav Stickley (1858-1942) was a leading light of American Arts and Crafts furniture design and manufacture: https://en.wikipedia.org/wiki/Gustav_Stickley Source: Objaverse 1.0 / Sketchfab
The phenotypic and fitness response to the combination of copper and thermal stressors strongly varies within the ciliate species, Tetrahymena thermophila
<p><span>Copper pollution can alter biological and trophic functions. Organisms can set up different tolerance strategies, including accumulation mechanisms (intracellular vacuoles, external chelation, etc.) to maintain themselves in copper-polluted environments. Accumulation mechanisms can influence the expression of other phenotypic traits, allowing organisms to improve their fitness. Whether copper effects on accumulation strategies interact with other environmental stressors such as temperature and how this may differ within species are still unsolved questions. Here, we tested experimentally whether the combined effect of copper and temperature modulates traits linked to fitness, morphology, movement and accumulation in six strains of the ciliate <em>Tetrahymena</em> <em>thermophila</em>. We also explored whether copper accumulation might modulate environmental copper concentration effects on phenotypic and fitness traits. Results showed high intraspecific variability in the phenotypic and fitness response to copper, with interactive effects between temperature and copper. In addition, they suggested an attenuation effect of copper accumulation on the sensitivity of traits to copper, but with great variation between strains, temperature and copper concentration. Diversity of responses among strains and their thermal dependencies pleads for the integration of intraspecific variability and multiple stressors approaches in ecotoxicological studies, thus improving the reliability of assessments of the effects of pollutants on biodiversity.</span></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.