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Thermoelectric sample Cu2.3Mn0.7GeS4 - M289 - Enargite phase
<p><strong><em>M289-Enargite</em></strong></p> <p>The following submission contains the data collection and processing of datasets for the sample M289, with composition Cu2.3Mn0.7GeS4 - Enargite phase, in the framework of the publication: Pavan Kumar, V., Passuti, S., Zhang, B., Fujii, S., Yoshizawa, K., Boullay, P., ... & Guilmeau, E. (2022). Engineering Transport Properties in Interconnected Enargite‐Stannite Type Cu2+ xMn1− xGeS4 Nanocomposites. <em>Angewandte Chemie International Edition</em>, <em>61</em>(49), e202210600.</p> <p>A crystal was identified, and precession data acquisition techniques were used to collect datasets on the same crystal. The data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder.</p> <p><strong>Precession:</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR12</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>Cu2+xMn1-xGeS4</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>Cu2.3Mn0.7GeS4</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>Cu2.3Mn0.7GeS4-Enargite-PEDT</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope Jeol F200</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>Cold FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Microdiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>70nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Cheetah M3 (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>200 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.00 716 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Enargite</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Cu<sub>2.3</sub>Mn<sub>0.7</sub>GeS<sub>4</sub></p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed on a mortar and dispersed in n-butanol, drop deposited on a Cu grid with holey C film</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>PEDT</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>102</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>-39.9° to +60.5°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p>1.2°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>500ms</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>Instamatic software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2 (ver 2.1.20211012.1037)</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Sara Passuti (ESR12)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p>Engineering Transport Properties in Interconnected Enargite-Stannite Type Cu2+xMn1-xGeS4 Nanocomposites</p> <p>DOI 10.1002/anie.202210600</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>raw-data\tiff: Folder containing images of the diffraction pattern from each frames.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>tiff_16bit</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>M289.tif : image of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>M289-Enargite.pts2 :input file for the program PETS2 used for processing the data</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p>
Data for manuscript, "An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples"
<p>Clinical BALF samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, MS based proteomic analysis of BALF is impeded by the dynamic range of protein abundance, and potential for interfering contaminants. We have developed a workflow that eliminates these challenges. By combining high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, our workflow is compatible with both qualitative and quantitative MS-based proteomic analysis. The workflow includes collection of endogenous peptides for peptidomic analysis of BALF, if desired, as well as amenability to offline semi-preparative or microscale fractionation of peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We show the effectiveness of this workflow on BALF samples from COPD patients. Overall, our workflow should allow MS-based proteomics to be applied to a wide variety of studies focused on BALF clinical samples. </p> <p>Note: Due to the nature of some of the files, file <em>wendt005_ostr0103_18260_20210831_BALF_FAIMS_MS2_TMT16.msf, wendt005_ostr0103_18976_20230202_quantReport.msf, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_1R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_2R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_3R.raw and cmsptc_higgi022_18988_20230203_18976DW_Eclipse_noFAIMS_quantReport.msf</em> were zipped into compressed folders before uploading.</p>
Thermoelectric sample Cu2.3Mn0.7GeS4 - M289 - Stannite phase
<p><strong><em>M289-Stannite</em></strong></p> <p>The following submission contains the data collection and processing of datasets for the sample M289, with composition Cu2.3Mn0.7GeS4 - Stannite phase, in the framework of the publication: Pavan Kumar, V., Passuti, S., Zhang, B., Fujii, S., Yoshizawa, K., Boullay, P., ... & Guilmeau, E. (2022). Engineering Transport Properties in Interconnected Enargite‐Stannite Type Cu2+ xMn1− xGeS4 Nanocomposites. <em>Angewandte Chemie International Edition</em>, <em>61</em>(49), e202210600.</p> <p>A crystal was identified, and precession data acquisition techniques were used to collect datasets on the same crystal. The data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder.</p> <p><strong>Precession:</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR12</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>Cu2+xMn1-xGeS4</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>Cu2.3Mn0.7GeS4</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>Cu2.3Mn0.7GeS4-Stannite-PEDT</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope Jeol F200</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>Cold FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Microdiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>70nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Cheetah M3 (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>200 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.00 716 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Stannite</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Cu<sub>2.3</sub>Mn<sub>0.7</sub>GeS<sub>4</sub></p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed on a mortar and dispersed in n-butanol, drop deposited on a Cu grid with holey C film</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>PEDT</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>99</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>-35.8° to +61.5°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p>1.2°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>500ms</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>Instamatic software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2 (ver 2.1.20211012.1037)</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Sara Passuti (ESR12)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p>Engineering Transport Properties in Interconnected Enargite-Stannite Type Cu2+xMn1-xGeS4 Nanocomposites</p> <p>DOI 10.1002/anie.202210600</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>raw-data\tiff: Folder containing images of the diffraction pattern from each frames.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>tiff_16bit</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>M289.tif : image of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>M289-Stannite.pts2 :input file for the program PETS2 used for processing the data</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p> </p> <p> </p> </td> </tr> </tbody> </table>
Data of Nutrition Knowledge in a Sample of College Students in Jordan
<p>This cross-sectional research assessed nutrition knowledge in a sample of college students in Amman, Jordan and its association with food security and other risk factors. An Arabic Nutrition Knowledge Index (ANKI) was developed and validated in 122 college students (study-1). In study-2, the demographics scale, validated ANKI, and Arabic Individual Food Insecurity Experience Scale (FIES) were administered to 470 students from the same university. </p> <p><strong>Ethical considerations:</strong>This manuscript has been read and approved by all authors. The authors confirm that there are no other persons, who satisfied the criteria for authorship, but are not listed. The order of authors listed in the manuscript has been approved by all of them. They also understand that the Corresponding Author is the sole contact for the Editorial process, and holds the responsibility for communicating with the other author about progress, submissions of revisions and final approval of proofs. Moreover, the authors declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. Furthermore, all data used in the study is confidential and the lead author has full access to the data reported in the manuscript. We confirm that there are no known conflicts of interest associated with this publication, which did not receive any financial support. Finally, the reporting of this work is compliant with The Code of Ethics of the World Medical Association (Declaration of Helsinki). In addition to that, the protocol of this research is approved by the Institutional Review Board at the University of Jordan, Amman, Jordan (ref no.: 2021-89).</p> <p> </p>
Tetiaroa Vegetation Sampling
<p>Measurement of seedling recruitment for four native woody species, the shade cover of four herbaceous plant species, and coconuts on the Motu Ahuroa in Tetiaroa were conducted by students with the UC Berkeley Island Sustainability Program. The four woody species we observed were Pisonia, Pandanus, Guettarda, and Ipomoea. For the herbaceous species, we observed Asplenium, Boerhavia, Microsorum, and Portulaca. Once we completed our data collection in each transect, we compiled our data to see the effect of rat eradication programs on plant growth.</p>
Raw data drosophila parasitoids sampled in 2008 life history traits
<p>Sampling of parasitoids in 2008 and 2011</p> <p>Life history and physiological traits measurements at different temperatures : developement time, fecundity, mass, metabolic rate, etc.</p> <p> </p>
The SafeSpace magnetospheric models sample forecast for the 2015 St Patrick's geomagnetic storm
<p>This dataset presents the sample forecast of the magnetosphere models in the SafeSpace project, for the March 2015 St Patrick's storm. It is build from a synthetic solar wind forecast at L1 and corresponding Kp forecast. This forecast if fed in the SPM plasma density model, as well as in a VLF wave intensities model, yielding the dataset presented here.</p> <p>All files are in the CDF file format.</p> <ul> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/GEOIDX_20150301.cdf">GEOIDX_20150301.cdf</a> file contains the synthetic solar wind and Kp ensemble forecast.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/Bw2_20150301.cdf">Bw2_20150301.cdf</a> file contains the corresponding VLF wave intensities.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SPM_dens_20150301.cdf">SPM_dens_20150301.cdf</a> file contains the corresponding plasma densities.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_A_Nowcast.cdf</a> and <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_B_Nowcast.cdf</a> files contains the reconstructed electron fluxes along the RBSP spacecrafts for the whole March 2015 month, using data assimilation in the SafeSpace pipeline.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_A_Forecast.cdf</a> and <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_B_Nowcast.cdf</a> files contains a 4 days forecast of the electron fluxes along the RBSP spacecrafts for March 17th to March 20th, 2015.</li> </ul> <p>This dataset and the SafeSpace pipeline is described in details in the article by Brunet et al. "Improving the electron radiation belt nowcast and forecast using the SafeSpace data assimilation modelling pipeline", currently in review in AGU Space Weather.</p> <p> </p>
Data from: Quantifying and estimating ecological network diversity based on incomplete sampling data
<p>An ecological network refers to the ecological interactions among sets of species. Quantification of ecological network diversity and related sampling/estimation challenges have explicit analogues in species diversity research. A unified framework based on Hill numbers and their generalizations was developed to quantify taxonomic, phylogenetic, and functional diversity. Drawing on this unified framework, we propose three dimensions of network diversity that incorporate the frequency (or strength) of interactions, species' phylogenies and traits. As with surveys in species inventories, nearly all network studies are based on sampling data and thus also suffer from under-sampling effects. Adapting the sampling/estimation theory and the iNEXT (interpolation/extrapolation) standardization developed for species diversity research, we propose the iNEXT.link method to analyze network sampling data. The proposed method integrates the following four inference procedures: (i) Assessment of sample completeness of networks, (ii) asymptotic analysis via estimating the true network diversity, (iii) non-asymptotic analysis based on standardizing sample completeness via rarefaction and extrapolation with network diversity, and (iv) estimation of the degree of unevenness or specialization in networks based on standardized diversity. Interaction data between European trees and saproxylic beetles are used for illustrating the proposed procedures. The software iNEXT.link is developed to facilitate all computations and graphics.</p>
Data sample INDIP Validation Paper
<p>Standardized data from Mobilise-D HYA dataset (one participant, data from both laboratory and 2.5 hours) and Mobilise-D technical validation dataset (one subject for each cohort - HOA, PD, MS, COPD, CHF, PFF; data from 2.5 hours) are provided in the shared folder, as an example of the the work proposed in the publication "A multi-sensor wearable system for the assessment of diseased gait in real-world conditions" that has been accepted for publication in Frontiers Journal. Please refer to that publication for further information. Please cite that publication if using these data. </p>
IODP Expedition 396 Sample report
<p>Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.</p>
Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate
Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).
Supplementary data for: Tackling hysteresis in conformational sampling --- how to be forgetful with MEMENTO
<p>This is the supplementary data for our publication entitled: "Tackling hysteresis in conformational sampling --- how to be forgetful with MEMENTO". This version corresponds to the first revision of our paper in response to reviewer feedback.</p> <p>Abstract:</p> <p><<<</p> <p>The structure of proteins has long been recognised to hold the key to understanding and engineering their function, and rapid advances in structural biology and protein structure prediction are now supplying researchers with an ever-increasing wealth of structural information. Most of the time, however, structures can only be determined in free energy minima, one at a time. While conformational flexibility may thus be inferred from static end-state structures, their interconversion mechanisms --- a central ambition of structural biology --- are often beyond the scope of direct experimentation. Given the dynamical nature of the processes in question, many studies have attempted to explore conformational transitions using molecular dynamics (MD). However, ensuring proper convergence and reversibility in the predicted transitions is extremely challenging. In particular, a commonly used technique to map out a path from a starting to a target conformation called steered MD (SMD) can suffer from starting-state dependence (hysteresis) when combined with techniques such as umbrella sampling (US) to compute the free energy profile of a transition. \\</p> <p> </p> <p>Here, we study this problem in detail on conformational changes of increasing complexity. We also present a new, history-independent approach that we term “MEMENTO” (Morphing End states by Modelling Ensembles with iNdependent TOpologies) to generate paths that alleviate hysteresis in the construction of conformational free energy profiles. MEMENTO utilises template-based structure modelling to restore physically reasonable protein conformations based on coordinate interpolation (morphing) as an ensemble of plausible intermediates, from which a smooth path is picked. We compare SMD and MEMENTO on well-characterized test cases (the toy peptide deca-alanine and the enzyme adenylate kinase) before discussing its use in more complicated systems (the kinase P38α and the bacterial leucine transporter LeuT). Our work shows that for all but the simplest systems SMD paths should not in general be used to seed umbrella sampling or related techniques, unless the paths are validated by consistent results from biased runs in opposite directions. MEMENTO, on the other hand performs well as a flexible tool to generate intermediate structures for umbrella sampling. We also demonstrate that extended end-state sampling combined with MEMENTO can aid the discovery of collective variables on a case-by-case basis.</p> <p>>>></p> <p>There are 4 folders in this dataset for the 4 simulation systems studied in the paper. Deca-alanine, ADK, P38a and LEUT. Each folder contains key coordinate files used to initialise MEMENTO and SMD runs, and data produced in the course of simulations (as labelled in the folders). In the simulation folders, 'raw_data' corresponds to the outputs produced by PLUMED during REUS or SMD, 'convergence' contains WHAM outputs taken at a certain percentage of the data included, and 'pointsfile' are the metadata files required by the Grossfield WHAM implementation.</p>
Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_213nm
<p>RHEED intensity image dataset of sample "<strong>treated_213nm"</strong> in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
IODP Expedition 354 Sample report
<p>Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.</p>
Exploring the Impact of Negative Sampling on Patent Citation Recommendation
<ul> <li> <p><strong>pcr_patents.csv </strong>is the dataset which is generated by collecting samples randomly from Google Patents by exploiting a <a href="https://pypi.org/project/google-patent-scraper/">Python library</a>. The dataset comprises around 250,000 US patents and their titles, abstracts, and citations. Each patent has roughly on average 27 citations.</p> </li> </ul> <p>The zip file contains 3 different datasets for training and testing patent citation recommendation systems. These datasets were generated by utilizing the main dataset. They consist of around 1 million instances which are positive as well as negative samples. </p> <ul> <li> <p><strong>pcr_cpc_negative_sample_data.csv</strong> consists of negative samples that were generated based on CPC subclass codes. </p> </li> <li> <p><strong>pcr_random_negative_sample_data.csv</strong> consists of negative samples that were generated randomly. </p> </li> <li> <p><strong>pcr_sem_sim_negative_sample_data_2.csv</strong> consists of negative samples that were generated based on nearest neighbor relation.</p> </li> </ul>
Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
<p>There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset.</p> <p>The <strong>tissue_classification </strong>file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets.</p> <p>The <strong>binary_lymphocytes </strong>file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset.</p> <p>All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous.</p> <p>The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) <strong>resnet18_binary_lymphocytes.pth</strong> and (2) <strong>resnet18_tissue_classification.pth</strong>. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.</p> <p> </p> <p>If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):<br><strong>(1) </strong><strong>Brummer, O., Pölönen, P., Mustjoki, S. <em>et al.</em> Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. <em>Br J Cancer</em> 129, 683–695 (2023). </strong><a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a></p> <p><strong>(2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: </strong><strong><a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a></strong><strong>.</strong></p>
Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback
<p><span>A main goal of evolutionary biology is to understand the genetic basis of adaptive evolution. Although the genes that underlie some adaptive phenotypes are now known, the molecular pathways and regulatory mechanisms mediating the phenotypic effects of those genes often remain a black box. Unveiling this black box is necessary to fully understand the genetic basis of adaptive phenotypes, and to understand why particular genes might be used during phenotypic evolution. Here, we investigated which genes and regulatory mechanisms are mediating the phenotypic effects of the <em>Eda</em> haplotype, a locus responsible for the loss of lateral plates and changes in the sensory lateral line of freshwater threespine stickleback (<em>Gasterosteus aculeatus</em>) populations. Using a combination of RNAseq and a cross design that isolated the Eda haplotype on a fixed genomic background, we found that the Eda haplotype affects both gene expression and alternative splicing of genes related to bone development, neuronal development and immunity. These include genes in conserved pathways, like the BMP, netrin and bradykinin signalling pathways, known to play a role in these biological processes. Furthermore, we found that differentially expressed and differentially spliced genes had different levels of connectivity and expression, suggesting that these factors might influence which regulatory mechanisms are used during phenotypic evolution. Taken together, these results provide a better understanding of the mechanisms mediating the effects of an important adaptive locus in stickleback and suggest that alternative splicing could be an important regulatory mechanism mediating adaptive phenotypes.</span></p>
CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples
<p>This folder includes simulated and real data used in validating CNETML, a new maximum likelihood method designed to reconstruct the evolutionary history of multiple samples of a single patient which may be taken at different locations and/or times, which can take as input (relative) total integer copy numbers called from shallow whole genome sequencing data.</p>
Sample images in .nd format - FIJI vs bfmatlab metadata
<p>Two sets of images acquired on a VTiSIM microscope (VisiTech). Each set contains an .nd header file and 4 accompanying .TIF files (DAPI, GFP, RFP and BF). DAPI, GFP and RFP .TIFs are z-stacks, BF is a single plane. Bio-Formats import through FIJI correctly collects the metadata. Bio-Formats import through MATLAB (bfmatlab package -bfopen()) incorrectly returns empty values for some metadata (e.g. Pixel Physical Size in Z).</p>
A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples
<p>This table contains peaks aera values from LC-MS used to develop a method for identification and quantification of anionic lipid in plant sample: Genva et al. 2023: “A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples”</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.