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FIGURE 10 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 10. Lalagetes zitae sp. nov. A—habitus, dorsal view, female; B—rostrum, female, dorsal view; C—rostrum, female, lateral view; D—protibia, female; E—aedeagus; F—spermatheca. Scales bars: 1 mm (A), 0.5 mm (E) and 0.3 mm (F).

FIGURE 5 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 5. Lalagetes andersoni sp. nov. A—habitus, dorsal view, female; B—rostrum, female, dorsal view; C—rostrum, female, lateral view; D—protibia, female; E—aedeagus; F—spermatheca. Scales bars: 1 mm (A), 0.5 mm (E) and 0.3 mm (F).

FIGURE 4 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 4. Distribution of Lalagetes species in South Africa; Lalagetes andersoni sp. nov. (green round); Lalagetes howdenorum sp. nov. (pink round); Lalagetes sarkae sp. nov. (yellow rounds); Lalagetes seminulum Fåhraeus, 1871 (red round); Lalagetes subfasciatus Boheman, 1842 (blue rounds); Lalagetes zitae sp. nov. (brown rounds).

FIGURE 3 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 3. Apex of penis. A—Lalagetes andersoni sp. nov.; B—Lalagetes howdenorum sp. nov.; C—Lalagetes sarkae sp. nov.; D—Lalagetes subfasciatus Boheman, 1842; E—Lalagetes zitae sp. nov. Scales bar: 0.25 mm.

FIGURE 2 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 2. Differential characters between genera Glyptosomus, Lalagetes and Phaylomerinthus: G. costipennis Schoenherr, 1847: A—ventrites, female; B—female sternite VIII; L. subfasciatus Boheman, 1842: C—ventrites, female; D—female sternite VIII; P. convergens (Voss, 1962): E—ventrites, female; P. cinereus Boheman, 1842: F—female sternite VIII; Scales bar: 0.5 mm (B, D, F).

FIGURE 1 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 1. Differential characters between genus Glyptosomus and genus Phaylomerinthus: G. costipennis Schoenherr, 1847: A—habitus, dorsal view, female; B—rostrum, female, dorsal view; C—protibia, male; P. cinereus Boheman, 1842: D— habitus, dorsal view, female; E—rostrum, female, dorsal view; F—protibia, female. Scales bar: 1 mm (A, D).

FIGURE 6 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 6. Lalagetes howdenorum sp. nov. A—habitus, dorsal view, female; B—rostrum, female, dorsal view; C—rostrum, female, lateral view; D—protibia, female; E—aedeagus; F—spermatheca. Scales bars: 1 mm (A), 0.5 mm (E) and 0.3 mm (F).

FIGURE 8 in Revision of the species related to Lalagetes subfasciatus Boheman and transfer of remaining Lalagetes species to other genera of Embrithini (Coleoptera: Curculionidae: Entiminae)

FIGURE 8. Lalagetes seminulum Fåhraeus, 1871. A—habitus, dorsal view, female; B—rostrum, female, dorsal view; C— rostrum, female, lateral view; D—protibia, female; E—spermatheca. Scales bars: 1 mm (A), and 0.3 mm (E).

Transposon DNA sequences facilitate the tissue-specific gene transfer of circulating tumor DNA between human cells

nuc_ctDNA_process ImageJ 1.x macros and Matlab code for processing 3D nuclear classification and quantification. This repo is designed to help you recreate the methods use in the associated publication. Please don't hesitate to contact if you have questions. Happy to debug, update, etc if there's need. Lif files: Use ImageJ 1.x macro in fiji folder to process lif files for subsequent ilastik and Matlab processing. Works with 3 channel data (DAPI, DIC, Rh-Red-X) and 4 channel data (DAPI, Cy5, Rh-Red-X, DIC). Generates .h5 or .tif files for ilastik raining, .jpgs for visualization and ROI overlays, and raw tif files for Matlab analysis. Macro Usage Drag and drop; click Run and select .lif of interest. Only 3D data will be included, single layer images will be noted in output. A table of dimensions and max intensities is also created. Save .csv image info, and .txt output log for reference. Organize Folder Structure Folders:  <ul> <li>Ilastik output</li> <li>Nuc</li> <li>Raw</li> <li>Roi</li> </ul>  ------------ <ul> <li>Place .h5 nuclear, or .tif nuclear and DIC, and .jpg thumbnail data in subfolder called “nuc”</li> <li>Place .tif raw data export into subfolder called “raw”</li> <li>Create subfolders “ilastik output” and “roi”</li> <li>Ilastik (version 1.3.2post1) trained with ~10-20% of datasets </li> <ul> <li>Ilastik side note: currently don't know how to share Ilastik projects without getting errors on loading for the given files and filepaths present during creation. You will need to train your own models. See NoPhotonLeftBehind for Ilastik series that includes training tips and details of features used for these data. <a href="https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ">https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ</a></li> </ul> <li>Models trained as Pixel Classifications – two classes, background and nucleus</li> <li>Ilsatik model trained to classify nuclear vs non nuclear – classical thresholding methods found to be less effective due to varying amounts on cytoplasmic DNA stain present.</li> <li>Single match and mismatch trained using nuclear channel only; double mismatch trained using nuclear and DIC channels together</li> <li>Data separated and models trained for each cell type due to distinct morphologies, e.g. MM1S model, HCT116 model, etc etc</li> <li>Probability density files </li> <ul> <li>Matlab looks for “*_nrmNuc.tiff“ in relative folder “.\ilastik output”, and this is the suffix added in the Fiji macro</li> <li>In ilastik, set output format to multipage tiff, and select path to .{nickname}.tiff. Note, use path of .{nickname}_nrmNuc.tiff if _nrmNuc is not added during your file collation and logistics to this point. Also note .tiff not .tif</li> <li>Leave image export settings as default; shape here is, for example, 16, 512, 512, 1, with axis order zyxc and data type float32</li> <li>In Batch Processing section, select all of the .h5 or .tif files in the “nuc” folder and Process all files</li> </ul> <li>Matlab UI </li> <ul> <li>Files Tab: </li> <ul> <li>Set Root – select folder containing “ilastik output”, “raw”, “roi”, and “nuc”</li> <li>Filename list will propagate, and Overview text at the top will highlight red if the correct number of files are not present in all folders. (TODO: - run test on error scenario to get instructions)</li> <li>Sig Num Chns – the total number of channels in the raw data tif files</li> <li>Rh/Cy5 Sig Chn – the 1 to N based index of the channel to measure inside the nucleus</li> <li>Rh/Cy5 Bkgd – the number of counts considered as background/cell autoflourescene/non-specific signal during measurements; only voxels with counts above this level will be included in the measurements</li> <li>ROI Num Chns – total number of channels in the ilastik probability density tiff files</li> <li>ROI Chn – 1 to N based index of channel to use for generating nuclear 3D ROIs</li> <li>Thumbnails on/off toggle when selecting images in list</li> <li>Currently only single or double channel analyses available (signal is measured inside and outside of nucleus 3D ROI)</li> <li>Click on files to view the nuc jpgs. Click Processing tab to experiment with settings. Note, above channel totals and indices do not currently have error checking. Check correct combinations if you receive tif read errors. Jpgs are loaded on each click, and raw is loaded on switching to Processing tab; expect short delay depending on file size and available disk read speeds.</li> <li>Open in Explorer button – no prizes for guessing that it opens the selected file in explorer. It defaults to the raw data.</li> <li>Process All button runs all the files using the settings in place in the Processing Tab. </li> <ul> <li>A dated folder in roi is created. Inside this folder there are four different types of output file:</li> </ul> </ul> <li>.bin – a binary mask of the 3D ROI</li> <li>_dims.bin – the dimensions of the binary mask</li> <li>.jpg – a thumbnail of ROI overlays</li> <li>.mat – parameters used for generating the ROIs (open .mat files, and click on the params variable in the Import Wizard to quickly view the relevant parameters) </li> <ul> <li>Use Masks dropdown: </li> <ul> <li>For faster re-processing of data with differing minimum number of voxels existing binary masks can be used</li> <li>Note, resulting .mat file in subsequent output will not reflect the parameters used to generate the binary masks – refer to the original folder (this is noted and will be added to newer versions)</li> </ul> <li> </li> </ul> <li>Processing tab: </li> <ul> <li>FFT % is the amount of Fourier space to keep; lower values retain low frequencies only – empirically determined for best resulting nuclear shape</li> <li>FFT Smooth value is Gaussian smoothing value in pixels applied to the ellipsoid mask used to retain the central region of Fourier space. Ringing can be seen for values close to 0, increase as needed.</li> <li>Gauss Smooth is the Gaussian smoothing applied to the raw prob data prior to Otsu thresholding. In noisy classifications thresholding leads to multiple fragmented regions; some smoothing prior to thresholding helps to ‘fuse’ these fragmented regions, prior to 3D FFT spatial filtering to smooth based on size.</li> <li>FFT xz factor is used to avoid smoothing nuclei in the z direction more than x and y. This value affects the ratio of xy and z of the 3D ellipsoid used to mask Fourier space. Set empirically; Click Run and then View Volume to inspect the z ‘stretch’.</li> <li>Button group options to apply different combinations of smoothing and FFT spatial filters: </li> <ul> <li>Gauss – uses Gauss Smooth value above; applied to raw prob data</li> <li>Otsu – Otsu binary threshold</li> <li>Fill – Binary fill applied after smooth and binarization</li> <li>FFT – 3D spatial filtering based on % of Fourier space</li> </ul> <li>Run, well, runs the analysis</li> <li>View Volume displays 3D viewer for resulting data set</li> <li>Min volume slider and value are used to exclude all 3D ROIs smaller than specified value; in voxels. Note slider is linear and plot is log.</li> </ul> <li>Notes: </li> <ul> <li>Requires Matlab 2018a or newer</li> <li>Requires Parallel Computing Toolbox for parfor loop in function ProcessAllButtonPushed. Change parfor to for if not available.</li> <li> </li> </ul> </ul> <li>Matlab filelist: </li> <ul> <li>*.mlapp</li> <li>import_tif.m</li> <li>bw_outline_p.m</li> <li>smth_otsu_fill_p.m</li> <li>LPFFT3D_p.m</li> <li>otsu_bw.m</li> <li>makepsd3.m</li> <li>ellipsoid_mask.m</li> <li>bin_load_mask.m</li> <li>process_ctDNA_table.m</li> <li>_p refers to passed param struct: </li> <ul> <li>wid = 3; % width of dilation in outline overlay</li> <li>pc; % percent of Fourier space to keep - smaller numbers -> more blurred out larger images</li> <li>pad = 1; % pad Fourier space to the next power of 2</li> <li>umpx = 0.09; % image pix size</li> <li>umpz = 0.3; % again in z</li> <li>fft_smth; % smoothing of the eliptical Fourier space mask</li> <li>gauss_smth; % sigma of Guass smooth for Guass, Otsu, Fill, BW</li> <li>scl = [1 1 1/0.3]; % scale ratios for volume viewer</li> <li>fft_xz_factor; % factor to increase or decrease the amount of z FFT smoothing compared to xy</li> <li>minvol = 0;</li> </ul> </ul> </ul>

ERC-20 and ERC-721 Transfer events

<h2>Description</h2> This repository contains information about the Transfer events emitted by ERC-20 and ERC-721 contracts on the Ethereum blockchain. Specifically, the events in the repository are those included in the first 15 million blocks of the chain, i.e., from block 0 (added on July 30th, 2015, 15:26:13 GMT) to block 14,999,999 (added on June 21st, 2022, 02:28:10 GMT). All files in the repository are written according to the CSV (Comma Separated Value) format. For space reasons, they have also been compressed using the XZ utility.  The dataset consists of the following files. <ul> <li><code>block_timestamps_0-14999999.csv.xz</code> contains information about the timestamps of individual blocks. Specifically, each row in the file represents a block and consists of 2 fields.   <ul> <li>The first field represents the block identifier (i.e., its height).</li> <li>The second field is the Unix epoch timestamp indicating the instant at which the block was added to the blockchain. </li> </ul> </li> <li>The <code>erc20_transfers.csv.xz</code> (resp. <code>erc721_transfers.csv.xz</code>) file contains a row for each ERC-20 (resp. ERC-721) Transfer event. The description of each transfer includes 4 fields.  <ul> <li>Block identifier in which the transfer occurred.</li> <li>Numeric identifier of the contract that produced the event.</li> <li>Numeric identifier of the sender of the transfer.</li> <li>Numeric identifier of the recipient of the transfer. </li> </ul> </li> <li>The <code>erc20_contracts.csv.xz</code> (resp. <code>erc721_contracts.csv.xz</code>) file contains the Ethereum addresses of the contracts that have raised at least one ERC-20 Transfer event (resp. ERC-721) within the considered blocks. More precisely, each row of these files represents a contract and consists of 2 fields. <ul> <li>Ethereum address of the contract. </li> <li>Numerical identifier used to represent the contract. Specifically, the identifiers listed in <code>erc20_contracts.csv.xz</code> are used inside <code>erc20_transfers.csv.xz</code>, while those of <code>erc721_contracts.csv.xz</code> are used in the <code>erc721_transfers.csv.xz</code> file. </li> </ul> </li> <li>The <code>erc20_addresses.csv.xz</code> (resp. <code>erc721_addresses.csv.xz</code>) file contains the Ethereum addresses of the participants involved in at least one ERC-20 Transfer event (resp. ERC-721) within the considered blocks. More precisely, each row of these files is composed of 2 fields. <ul> <li>Ethereum address of the participant. </li> <li>Numerical identifier used to represent the participant. The identifiers contained in <code>erc20_addresses.csv.xz</code> are used in the <code>erc20_transfers.csv.xz</code> file, while those of <code>erc721_addresses.csv.xz</code> are used in the <code>erc721_transfers.csv.xz</code> file. </li> </ul> </li> <li>The <code>erc20_values_dec.csv.xz</code> file contains the amounts of all ERC-20 token transfers. The file includes a row for each ERC-20 transfer and the n-th row contains the amount of fungible tokens exchanged in the n-th transfer of the <code>erc20_transfers.csv.xz</code> file. </li> <li>The <code>erc721_values_dec.csv.xz</code> file has one row for each ERC-721 token transfer. The n-th row of this file contains the identifier of the non-fungible token exchanged in the n-th transfer of the <code>erc721_transfers.csv.xz</code> file.</li> </ul> <h2>Cite this work  </h2> If the data included in this repository have been useful, please cite the following articles in your work. <pre><code>@article{LoporchioMBR24, title = "Comparing Ethereum fungible and non-fungible tokens: an analysis of transfer networks", author = "Loporchio, Matteo and Di Francesco Maesa, Damiano and Bernasconi, Anna and Ricci, Laura", journal = "Applied Network Science", volume = "9", number = "1", pages = "72", year = 2024 }</code></pre> <pre><code>@incollection{Loporchio2024-gb, title = "Analysis and characterization of {ERC-20} token network topologies", booktitle = "Complex Networks \& Their Applications {XII}", author = "Loporchio, Matteo and Di Francesco Maesa, Damiano and Bernasconi, Anna and Ricci, Laura", publisher = "Springer Nature Switzerland", pages = "344--355", series = "Studies in computational intelligence", year = 2024, address = "Cham" }</code></pre> <h2>Funding</h2> This work was partially supported by Project Awesome: Analysis Framework for Web3 SOcial MEdia, project code: 2022MAWEZA, under the National Recovery and Resilience Plan funded by the European Union - NextGenerationEU, MISSION 4 COMPONENT 2, INVESTMENT N.1.1 CALL PRIN 2022 D.D. 104 02-02-2022, CUP N.I53D23003680006, and by Project DLT-FRUIT: A user centered framework for facilitating DLTs FRUITion, project code: P2022NZPJA, under the National Recovery and Resilience Plan funded by the European Union - NextGenerationEU, MISSION 4 COMPONENT 2, INVEST- MENT N.1.1 CALL PRIN 2022 PNRR D.D. n. 1409 14/09/2022, CUP N.I53D23006100001. <h2>References</h2> [1] M. Loporchio, D. Di Francesco Maesa, A. Bernasconi, and L. Ricci, “Analysis and Characterization of ERC-20 Token Network Topologies,” Complex Networks & Their Applications XII. Springer Nature Switzerland, pp. 344–355, 2024. <a href="https://doi.org/10.1007/978-3-031-53472-0_29" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-53472-0_29</a> [2] M. Loporchio, D. Di Francesco Maesa, A. Bernasconi, and L. Ricci, "Comparing Ethereum Fungible and Non-Fungible Tokens: an Analysis of Transfer Networks," Applied Network Science, vol. 9, no. 1. Springer Science and Business Media LLC, Nov. 20, 2024. <a href="https://doi.org/10.1007/s41109-024-00682-8" target="_blank" rel="noopener">https://doi.org/10.1007/s41109-024-00682-8</a>

Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

This zip file contains datasets for training Behler-Parrinello neural networks using the n2p2 software. Both DFT and VQE training sets are given for both the water monomer and dimer dataset.

opencc-by-4.0Dec 2023View details →

GIST: Generated Inputs Sets Transferability in Deep Learning (Part 2)

Part2 of the Replication Package for the paper "GIST: Generated Inputs Sets Transferability in Deep Learning" Contains RoBERTa models and data for the KMNC property. Github link: https://github.com/FlowSs/GIST Part1 can be found here: https://zenodo.org/records/10028594 Abstract:    To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods for test case generation techniques are being developed.     When confronted with testing DNN models, the user can apply any existing test generation technique. However, it needs to do so for each technique and each DNN model under test, which can be expensive. Therefore, a paradigm shift could benefit this testing process: rather than regenerating the test set independently for each DNN model under test, we could transfer from existing DNN models.      This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficient transfer of test sets. Given a property selected by a user (e.g., neurons covered, faults), GIST enables the selection of good test sets from the point of view of this property among available test sets. This allows the user to recover similar properties on the transferred test sets as he would have obtained by generating the test set from scratch with a test cases generation technique. Experimental results show that GIST can select effective test sets for the given property to transfer. Moreover, GIST scales better than reapplying test case generation techniques from scratch on DNN models under test.

Dataset for Assessing Charge Transfer Characteristics of Triphenylamine Derivatives

This repository comprises various essential components, including Cartesian coordinates, input files, properties files generated by Orca, and a Python script enabling the complete recreation of the results. The repository contains four main types of files: <ol> <li>Orca Inputs: These are utilized to compute properties of hole transporting materials associated to the power efficiency conversion of perovskite solar cells, such as reorganization energies, chemical reactivity parameters and transfer integrals.</li> <li>DFTB+ Inputs: These files facilitate the optimization of molecular structures using the tight-binding method DFTB3-3ob-MBD.</li> <li>Optimized Conformers:  Each file contains a set of conformers (xyz format) obtained through the global optimization procedure conducted with CREST.</li> <li>Python Script: This script calculates transfer integrals using the DIPRO method.</li> </ol> Additionally, the repository includes r2scan-3c Orca gradient files (which encompass energy, gradient, and Cartesian coordinates in Bohr units), DFTB3-3ob-MBD optimized geometries, TD-DFT-M06/6-31G(d,p) Orca outputs, and properties files generated by Orca during the reorganization energy calculation step. Requirements To effectively utilize the contents of this repository, ensure you have the following: <ul> <li>The latest release of Orca, which can be downloaded for free from the official site: https://orcaforum.kofo.mpg.de/</li> <li>Python, along with the cclib library, available at https://cclib.github.io. Ensure you have Python (version 3.7 or higher) and NumPy (version 1.15 or higher) installed.</li> <li>The latest releases of CREST and xtb, both accessible from the Grimme Lab GitHub page: https://github.com/grimme-lab</li> <li>The most recent version of DFTB+, which can be obtained from DFTB+ Official Site: https://dftbplus.org</li> </ul> These requirements are necessary for replicating and utilizing the results and functionalities provided by the repository effectively. Python script The python code files extract the data from Orca file output and perform the matrix and vector products to calculate and display the transfer integral. The python script require three arguments: the momomer A and B and dimer orca output files:      python Jeff-hole.py A.out B.out dimer.out Acknowledgments Raul Flores acknowledges CONAHCYT for the postdoctoral fellowship (CVU: 365229). The authors gratefully acknowledge the computing time granted by LANCAD and CONAHCYT on the supercomputer Miztli at DGTIC UNAM.

Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"

<div> This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"   </div>

opencc-by-4.0Jun 2024View details →

Strain partitioning, transfer and implications for ongoing processes of the intra-continental graben formation in the NW margin of the Ordos block, China: insights from densified GNSS measurements

GNSS data and processing 1. Intensive observations In this study, in addition to GNSS data surveyed around the Ordos block provided by Hao et al. (2021), we collected GNSS data surveyed west of 103°E from Phases I and II of the Crustal Movement Observation Network of China (CMONOC), which has conducted measurements every one or two years from 1999 to 2019. In particular, we also collected data from 70 GNSS sites deployed by <a name="OLE_LINK49"></a><a name="OLE_LINK50"></a><a name="OLE_LINK82"></a>the National Geodetic Control Network of China (NGCNC). These sites were distributed in the northwestern boundary of the Ordos block and the interior of the Alashan and Ejina blocks and first measured 72 hours of data in 2014 or 2015. From September to October 2021, we conducted the second set of measurements for these GNSS sites of 96 continuous hours.   2. Data processing The GAMIT package (Herring et al., 2015a) was employed to process the double-differenced carrier phase observations, and daily loosely constrained solutions were obtained. The primary geophysical models and parameters used in the processing, such as the FES2004 tidal loading model, GPT2 global pressure and temperature model (Lagler et al., 2013), and VMF1 mapping function (Boehm et al., 2006), are the same as those in Hao et al. (2021). Instead of incorporating global daily solutions provided by the IGS analysis centers, we also processed data from ~70 evenly distributed global International Terrestrial Reference Frame (ITRF) core tracking GNSS sites using the GAMIT package with the same models. We estimated daily coordinates and uncertainties by constraining the daily regional solutions with global solutions, and the daily free network solutions were transformed into the ITRF2014 (Altamimi et al., 2017) reference frame by utilizing the GLOBK package (Herring et al., 2015b). For campaign GNSS sites, the weighted least-squares adjustment was used to solve the linear velocity with respect to the ITRF2014. To analyze the differential crustal motion between blocks, we selected the stable Ordos block as the regional reference frame and transformed the GNSS velocity field referenced to the ITRF2014 into the Ordos-fixed frame through Euler rotation. The absolute Euler pole of the Ordos block is located at 75.083°N ± 0.850° and 139.692°W ± 3.701°, with an angular rotation rate of 0.352 ± 0.006°/Ma (Hao et al., 2021).   References Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model.Geophys. J. Int. 209:1906–1912 Boehm, J., Werl, B., and Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. Journal of Geophysical Research, 111, B0240<a href="https://doi.org/10.1029/2005JB003629">. doi:10.1029/2005JB003629</a>. Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021.“Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008. Herring, T.A., King, R.W., McClusky, S.C., 2015b.GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6.Massachusetts Institute of Technology, Cambridge. Herring, T.A., King, R.W.,McClusky, S.C., 2015a.GAMIT reference manual, GPS analysis at MIT, Release 10.6.Massachusetts Institute of Technology, Cambridge. Lagler, K., Schindelegger, M., Böhm, J., Krásná, H., and Nilsson, T. (2013). GPT2: empirical slant delay model for radio space geodetic techniques. GeophysicalResearchLetters, 40, 1069-1073. doi: 10.1002/grl.50288.

opencc-by-4.0Jun 2024View details →

IR data of the compounds published in "Replacement of Molybdenum by Tungsten in a Biomimetic Complex Leads to an Increase in Oxygen Atom Transfer Catalytic Activity"

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2024View details →

Transfer time for MPs modeled using TrackMPD

This file contains the information needed to run the code on github (https://github.com/carlahajjar1/MPs_FFs_CFs)

opencc-by-4.0Jul 2024View details →

One Hand project - Transfer of prosthesis control skill after training in VR, data set pre-test post-test

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2024View details →

Data Related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"

Data related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"