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Effects of Soil Warming and Substrate Complexity on Microbial Carbon Use Efficiency at Harvard Forest 2017
Soil microbial carbon use efficiency (CUE) is a combination of growth and respiration, which may respond differently to climate change depending on physical protection of soil carbon (C) and its availability to microbes. In a mid-latitude hardwood forest in central Massachusetts, 27 years of soil warming (+5 ◦C) has resulted in C loss and altered soil organic matter (SOM) quality, yet the underlying mechanisms remain unclear. Here, we hypothesized that long-term warming reduces physical aggregate protection of SOM, microbial CUE, and its temperature sensitivity. Soil was separated into macroaggregate (250–2000 μm) and microaggregate (less than 250 μm) fractions, and CUE was measured with 18O-enriched water in samples incubated at 15 and 25 ◦C for 24 h. We found that long-term warming reduced soil C and nitrogen concentrations and extracellular enzyme activity in macroaggregates, but did not affect physical protection of SOM. Long-term warming showed little effect on CUE or microbial biomass turnover time because it reduced both growth and respiration. However, CUE was less temperature sensitive in macroaggregates from the warmed compared to the control plots. Our findings suggest that microbial thermal responses to long-term warming occur mostly in soil compartments where SOM is less physically protected and thus more vulnerable to microbial degradation.
Helical dinuclear 3d metal complexes with bis(bidentate) [S,N] ligands: synthesis, structural and computational studies
<h1>Raw data for the publication entitled:</h1> <h2>Helical dinuclear 3d metal complexes with bis(bidentate)<br>[S,N] ligands: synthesis, structural and computational<br>studies</h2> <p><em>Dalton Transactions</em>, <strong>2024</strong>, DOI: 10.1039/D4DT02395A</p> <p>Authors:<br>Jamie Allen, Jörg Saßmannshausen, Kuldip Singh, Alexander F. R. Kilpatrick*</p> <p>These folders contain the raw data which were used to prepare the above publication.</p> <h1>Information regarding the raw files of the DFT calculations.</h1> <p>The zip-files in this section containing the raw-data of the DFT calculations leading to the Zn, Co and Fe calculated structures. As filenames are notoriously bad in handling special characters, the names of the folder appear different from what is being used in the final publication. We try to provide as much information as possible to facilitate the usage of these results.</p> <p>Thus:</p> <table> <tbody> <tr> <th>Abbreviation publication</th> <th>Abbreviation folder</th> <th>Abbreviation filename</th> </tr> </tbody> <tbody> <tr> <td>[Zn(<strong>3</strong>)<sub>2</sub>]</td> <td>Zn3-2</td> <td>SNdipp2Zn</td> </tr> <tr> <td>[Co(<strong>3</strong>) <sub>2</sub>]</td> <td>Co3-2</td> <td>SNdipp2Co</td> </tr> <tr> <td>[Fe(<strong>3</strong>) <sub>2</sub>]</td> <td>Fe3-2</td> <td>SNdipp2Fe</td> </tr> <tr> <td>[Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Zn2-2</td> <td>zn2</td> </tr> <tr> <td>[Co<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Co2-2</td> <td>co2</td> </tr> <tr> <td>[Fe<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Fe2-2</td> <td>fe2</td> </tr> </tbody> </table> <p>Some test calculations were performed as well utilizing Gaussian-09. They can be found in a folders with the suffix <em>-G09</em> or <em>-g09</em>.</p> <p>The closed shell compound [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] was investigated further. In order to look into the influence of the used Grimme dispersion correction, we re-calculated the final result without that correction. These files are in the Zn2-2-pbe0 folder. Furthermore, we used [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] and removed one of the Zn atoms and replaced the dangling bonds with H. We then fully optimized that structure. The results are in the Zn2-2-cut folder.</p> <h1> </h1> <h1>Information regarding the raw characterisation data</h1> <p>The raw characterisation data files for all nuclear magnetic resonance (NMR) spectroscopy, infrared (IR) spectroscopy, cyclic voltammetry (CV), single crystal X-ray diffraction (XRD) and solution magnetometry studies are enclosed in separate .zip files.</p>
Mycorrhizal fungal communities identified from seedlings planted in the Taylor, Dalton, and Boundary fire complexes which burned in 2004
This dataset contains the operational taxonomic unit table and taxonomic assignments for fungi that were associated with the roots of seedlings planted into the 2004 burn sites. There were 458 seedlings from 22 of the 32 established intensive sites (Johnstone and Hollingsworth 2019) consistenting of black spruce, white spruce, aspen, and lodgepole pine.
Datasets for: Generalizing Monin-Obukhov Similarity Theory (1954) for Complex Atmospheric Turbulence, Stiperski and Calaf 2023, PRL
<p>Scaling variables for the generalized flux-variance scaling relations that include turbulence anisotropy. Dataset is a companion to the manuscript Stiperski, I., Calaf, M., 2023: Generalizing Monin-Obukhov similarity theory (1954) for complex atmospheric turbulence. Physical Review Letters, 130 (12), 124001, https://doi.org/10.1103/PhysRevLett.130.124001</p> <p>The dataset contains the turbulence statistics from 13 datasets: AHATS, Cabauw, CASES-99, METCRAX II campaign (NEAR and RIM towers), T-Rex campaign (Central tower - TRexC, West tower - TRexW) and i-Box measurement network (CCS-VF0 tower - i-Box0, CS-SF1 tower - i-Box1, CS-NF10 tower - i-Box10, CS-NF27 tower - i-Box27, CS-MT21 tower - i-BoxTop, im Hinteren Eis tower - imHint).</p> <p><br>Data are organized in csv files for each datasets and only contain high quality (for applied criteria see the Supplemental Material of the companion paper, https://journals.aps.org/prl/supplemental/10.1103/PhysRevLett.130.124001) data with 30 min averaging for unstable stratification and 1 min for stable stratification. Since the data were used for scaling, there is no reference to time, but the measurement height is provided as an additional variable. </p> <p>Meaning of variables:</p> <p>zeta - z/L where z is height above ground and L is the local Obukhov length</p> <p>SigmaU - $\overline{u'u'}/u_*$ scaled standard deviation of streamwise velocity, where $u_*$ is the local friction velocity</p> <p>SigmaU - $\overline{v'v'}/u_*$ scaled standard deviation of spanwise velocity</p> <p>SigmaU - $\overline{v'v'}/u_*$ scaled standard deviation of surface-normal velocity</p> <p>SigmaT - $\overline{T'T'}/T_*$ scaled standard deviation of sonic temperature, where $T_*$ is the local temperature scale</p> <p>SigmaEpsU - scaled dissipation rate of the streamwise velocity</p> <p>SigmaEpsW - scaled dissipation rate of the surface-normal velocity </p>
Supplementary dataset to publication: Complete Genome Sequence of Ovine Mycobacterium avium subsp. paratuberculosis Strain JIII-386 (MAP-S/type III) and Its Comparison to MAP-S/type I, MAP-C, and M. avium Complex Genomes.
<p>This is the modified supplemented material to the publication “Complete genome sequence of ovine Mycobacterium avium subsp. paratuberculosis strain JIII-386 (MAP-S/type III) and its comparison to MAP-S/type I, MAP-C, and M. avium complex genomes”.</p> <p>The complete circular genome of Mycobacterium avium subsp. paratuberculosis (MAP) strain JIII-386 from Germany, closed by Nanopore technology in this study, was presented and compared with the draft genome of JIII-386, previously published in [doi:10.1093/gbe/ew154], the closed genome of the MAP-S/type I strain Telford, the MAP-S/type III draft genome of strain S397, twelve closed MAP-C (type II) strains and eight closed Mycobacterium avium (M. a.) strains of subsp. hominissuis (MAH) and subsp. avium (MAA). Structural comparisons clearly revealed the mosaic nature of MAP genomes, the differences between MAP subtypes I, II and III, and the higher diversity of MAP-S compared to MAP-C genomes. </p> <p>The material provides a wealth of detailed results from these analyses and comparisons. These include a list of identified ncRNA and Riboswitches, as well as additional genes in finished JIII-386, the gene content of identified prophage regions, copy number of identified transposable elements and a list of selected virulence-associated genes in the different MAP-type (I - III) strains. The genomic islands identified and included genes along with their predicted functions were presented for six MAP genomes (belonging to MAP-S/type I and III, and MAP-C), one MAH genome and one MAA genome. One table shows the corresponding genomic islands in the genomes of JIII-386, Telford and three MAP-C genomes. Furthermore, homologous genes of known MAP-S specific Large Sequence Polymorphisms regions (LSP<sup>S</sup> = LSP-S) were recorded in different MAP-S type strains, one MAH and one MAA strain, as well as genes of deletions #1 (LSP<sup>A</sup>-20), #2, and s-delta-1, previously described as MAP-S-specific deletions, their presence or absence in 3 MAP-S, 12 MAP-C, 4 MAH, and 4 MAA strains were listed. Different presence or absence of genes, but also identified frameshifts or disruptions of various virulence-associated genes could lead to the different MAP-type specific phenotypic characteristics. Comprehensive core and pan genome analyses (results listed in six tables) revealed unique genes and genes likely to have been acquired by horizontal gene transfer in different MAP types and subtypes, but also emphasized the highly conserved and close relationship, and the complex evolution of M. a. strains.</p> <p> </p>
Drone onboard multi-modal sensor dataset for complex outdoor scenarios
<p>The Data acquisition missions were designed and executed using DJI Pilot 2’s flight route planning feature. The missions encompassed five distinct geometric patterns: 1. triangular, 2. circular, 3. rectangular, 4. linear, and 5. multi-dimensional. Each mission was configured as a waypoint flight path, allowing precise customization of parameters such as altitude, speed, and turning angle for each waypoint. The dataset consists of 3D space flight data such as take-off, landing and varying altitude to introduce the z-axis changes. It must be noted that data was logged at a frequency of 10 Hz.</p> <p>To ensure consistency within the data, identical parameters were maintained across all data acquisition missions. The dataset comprises 20 distinct flights, with each flight path repeated multiple times, resulting in approximately 30 minutes of flight time per mission. The dataset is structured as time-series data, with each flight uniquely identified by a flight number and corresponding timestamp. The drone's spatial position is represented by the variables <strong>position_x, position_y, position_z </strong>while its orientation is captured by the variables <strong>orientation_x, orientation_y, orientation_z, orientation_w</strong>. Additionally, the drone's velocity and angular velocity are represented by the variables <strong>velocity_x, velocity_y, velocity_z, angular_x, angular_y, angular_z </strong>respectively. The linear acceleration is described by the variables <strong>linear_acceleration_x, linear_acceleration_y, linear_acceleration_z</strong>. The dataset also includes environmental data such as <strong>wind_speed, wind_angle </strong>using the TriSonica Mini Wind and Weather Sensor as well as information regarding the drone's battery status, including <strong>battery_voltage, battery_current.</strong></p> <p><strong>Data Acquisition Paths: <a href="https://ucy-my.sharepoint.com/:i:/g/personal/ygrigo01_ucy_ac_cy/EYAgdcLGCWxPloO1NMnsF-8Btf390Kmx854IuDe9R3E1ig?e=3Trbuk">Data acquisition paths</a></strong></p> <p>The dataset includes labels for various operational states of the drone, such as IDLE_HOVER, ASCEND, TURN, HMSL and DESCEND. These labels can be utilized to classify the drone's current activity. Moreover, the annotated dataset can be applied in multi-task learning to predict the drone's trajectory.</p> <p>The DJI Matrice 300 RTK is utilized as the primary platform for data acquisition, leveraging its compatibility with onboard development kits to facilitate the extraction of data from its integrated sensors and flight controller. To execute the developed software the NVIDIA Jetson Xavier NX serves as the embedded computing device. Utilizing the Onboard software development kit the Jetson Xavier NX enables real-time access and processing of data from the drone's sensors and flight controller.</p>
Natural frequency of oscillations of a solid surface (without holes) and perforated sieve with holes of complex geometry in the shape of five-petal epicycloid
<p>The experimental determination of the structural function of the frequency response consists in identifying the natural frequencies of oscillation of the test surfaces, for which the laboratory equipment was developed, and the following methodology was used. </p> <p>To determine the structural function of the frequency response, it is necessary to obtain two data channels: the input force and the corresponding response of the test object (test surface). In impact measurement, the input force is provided by a modal impact hammer, and the output response of the test object (test surface) is measured using an accelerometer.<br>The basic elements of the scheme are a special impact pulse type hammer PCB 084A17 for creating excitations (oscillations); cables for signals transmission; accelerometer sensor PCB 352V10 with highly sensitive piezoelectric elements for fixing oscillations; signal amplifier SIEMENS model SCADAS Mobile; computer with Simcenter Testlab 2019.1 software for processing and visualization test results.</p> <p>The study was conducted according to the following algorithms:<br>1. Test setup: boundary conditions; determination of test scheme and parameters; frequency range; determination of excitation source and force level.<br>2. Testing: installation and control of accelerometers; object excitation and frequency response measurement; check of measurement quality and coherence.<br>3. Post-test: modal curve fitting; validation of the modality against the assurance criterion and modal synthesis.<br>The research was carried out using the following algorithm. </p> <p>The perforated surface prototype was rigidly fixed to the prefabricated frame. With this type of fixation, the investigated surface at the periphery is fixed and unable to move.<br>The surface of the prototype was marked by overlaying a coordinate grid with the specified step.<br>This data of natural frequency of oscillations of a solid surface under various modes, which are obtained experimentally. The obtained oscillation frequencies are needed to determine the difference between the construction of a solid plate and a perforated surface with holes of complex geometry.</p>
Structures of S-protein in complex with ligands deposited in the PDB between the 1st January 2021 and the 13th May 2021
<p>All 174 structures of SARS-CoV-2 S-protein in complex with a ligand released on the PDB between the 1<sup>st</sup> January 2021 and the 13<sup>th</sup> May 2021 were categorised by ligand type: hACE2, antibody Fab fragments, VHH antibody fragments or <em>de novo</em> designed peptide scaffolds. Information concerning the method by which the structures were determined and their resolution were retrieved from the PDB. The categorisation of ligands by S-protein binding site were achieved by visual analysis of all the structures using molecular visualisation software PyMOL, in which no new binding sites were found beyond those already categorised for the structures released on the PDB until the 1<sup>st</sup> January 2021 (10.5281/zenodo.5503855).</p> <p>The Pure project is funded by the European Union’s Horizon 2020 program under grant agreement No. 899732.</p> <p> </p>
List of the structures of S-protein in complex with ligands deposited in the Protein Data Bank until the 1st January 2021.
<p>All 131 structures of SARS-CoV-2 S-protein in complex with a ligand released on the PDB until the 1<sup>st</sup> January 2021 were categorised by ligand type: hACE2, antibody Fab fragments, VHH antibody fragments or <em>de novo</em> designed peptide scaffolds. The ligands’ amino acid sequences, the method by which the structures were determined and their resolution were retrieved from the PDB. Information regarding the ligands' production method, dissociation constants (K<sub>D</sub>), S-protein segment against which the K<sub>D</sub> were measured and the determination methods were retrieved from the respective references. The categorisation of ligands by S-protein binding site and listing of S-protein conformation in each structure were achieved by visual analysis of all the structures using molecular visualisation software PyMOL.</p>
Datasets and R-scripts used for the revision of the Dibrachys cavus complex by Peters & Baur, 2011, Zootaxa 2937.1
<p>In 2011 we published a revision on the Dibrachys cavus complex in Zootaxa (Peters and Baur, 2011, here a link to our <a href="https://doi.org/10.11646/zootaxa.2937.1.1">open access paper</a>). It was our wish to also publish two versions of the dataset (one with missing values, one with missing values imputed) as supplementary files. Unfortunately, the data files seem to be no longer available on the publishers webpage. Hence, we publish the data files herewith again in CSV format.</p> <p>We take the opportunity to also publish the R-scripts that we used for calculating multivariate analyses, tests, and the multiple imputation of missing values.</p> <p>All files are available individually and with an own link. For convenience, we have compiled all files also in a ZIP file.</p> <p><a href="https://doi.org/10.5281/zenodo.4256704">Baur (2020)</a> used the dataset for further exploration in a Multivariate Ratio Analysis (MRA).</p> <p>Papers quoted above you may find in the section <em>References</em> of the Zenodo package.</p> <p><strong>Citation of this package</strong><br> Peters, Ralph S., & Baur, Hannes (2020, November 9) Datasets and R-scripts used for the revision of the Dibrachys cavus complex by Peters & Baur, 2011, Zootaxa 2937.1. Zenodo. https://doi.org/10.5281/zenodo.4264539 (directs to the newest version of the package).</p>
Phylogeny of "Philoceanus complex" seabird lice (Phthiraptera: Ischnocera) inferred from mitochondrial DNA sequences
<p>Data from "Phylogeny of “<em>Philoceanus </em>complex” seabird lice (Phthiraptera: Ischnocera) inferred from mitochondrial DNA sequences". See the file index.html for details. Data includes NEXUS files for sequences, tree files output by MrBayes and PAUP, and host-parasite association files for TreeMap.</p>
iRead4Skills - Basic Lexicons per Complexity Level
<div> <p>The iRead4Skills Basic lexicons per Complexity Level consists of three basic lexicons per complexity level for French, Spanish, and Portuguese, provided in .xlsx format. These lexicons were compiled under the scope of the project iReadSkills – Intelligent Reading Improvement System for Fundamental and Transversal Skills Development, funded by the European Commission (grant number: 1010094837). The project aims to enhance reading skills within the adult population by creating an intelligent system that assesses text complexity and recommends suitable reading materials to adults with low literacy skills, contributing to reducing skills gaps and facilitating access to information and culture (https://iread4skills.com/).</p> </div> <div> <p>Each lexicon covers the complexity levels deemed relevant for the project - Very Easy (approximately A1), Easy (approximately A2), and Plain (approximately B1) -, and will contribute to the complexity analysis systems for the three languages of the project: French, Portuguese, and Spanish. The data files are accompanied by a description of the data. The baselines for each lexicon definition can be consulted here: iRead4Skills - Baselines for complexity lexicons definition (<a href="https://doi.org/10.5281/zenodo.10069793" target="_blank" rel="noreferrer noopener">https://doi.org/10.5281/zenodo.10069793</a>)</p> <p> </p> </div> <div> <p><strong>French lexicon</strong>: 10103 entries </p> </div> <div> <p><strong>Portuguese lexicon</strong>: 2 729 entries</p> </div> <div> <p><strong>Spanish lexicon</strong>: 3 033 entries </p> </div>
House complex. Now hotel restaurant "El Buffi". 1929. Modernism.
<u>File Name</u>: PM_072999_E_Solsona <br><u>Sublocation</u>: Plaça de Sant Roc <br><u>Location</u>: Solsona <br><u>Province</u>: Catalunya, Lleida <br><u>Country</u>: Spain <br><u>Header</u>: Restaurant el buffi <br><u>Description</u>: House complex. Now hotel restaurant "El Buffi". 1929. Modernism. <br><u>Author</u>: photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert; pmrmaeyaert@gmail.com <br><u>Keywords</u>: Cultural heritage|Monuments; Cultural heritage|Monuments|Private house; Cultural heritage|Styles; Cultural heritage|Styles|Eclecticism; Cultural heritage|Styles|Modernism; Europe|Spain; Europe|Spain|Catalunya; Europe|Spain|Catalunya|Lleida; Europe|Spain|Catalunya|Lleida|Solsona; Cultural heritage <br><u>Date of Generation</u>: 2012-06-12T11:35:06.064
Synthetic cryo electron subtomograms containing biomolecular complexes with continuous conformational variability, used for validating TomoFlow method
<p>Two datasets used for validating TomoFlow method, an optical-flow based approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron subtomograms. The TomoFlow method and the methods used to synthesize the two test datasets have been fully described in the following article: "M. Harastani, M. Eltsov, A. Leforestier, S. Jonic, TomoFlow: Analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow, Journal of Molecular Biology (2021), doi: https://doi.org/10.1016/j.jmb.2021.167381". Additionally, this article describes a test of TomoFlow using one experimental cryo electron tomography dataset (available in EMPIAR and EMDB databases under the accession codes EMPIAR-10679 and EMD-12699). </p>
A phenomenological law for complex granular materials from Mohr-Coulomb theory
<p>The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3). </p>
MUHAI Benchmark : Task 3 (Understanding Complex Concepts)
<p><strong>Meaning and Understanding in Human-Centric AI (MUHAI) Benchmark<br> Task 3 Understanding complex concepts</strong></p> <p> </p> <p>This dataset helps investigating whether symbolic reasoning can help statistical models tro understand complex concepts. Complex concepts are expressed in the form of Image Schemas (i.e., mental templates that summarise human experiences in the form of patterns of object relations and actions).<br> The submission includes the the ImageSchema dataset with ground truth labels :<br> 1. A question to be asked<br> 2. the type of Image schema (class)<br> 3. the type of phrasing (literal , metaphoric, a distracting sentence)<br> 4. the type of questioning (one referring to the image schema by name, and another describing its content) <br> 5. a question indicating whether it is a yes or no answer<br> 6. the image schema variables identified<br> <br> Each sample in the datasets starts with a question about the presence of the given schema in the following sentence, and follows with a single sentence to be classified as either "yes" or "no" (presence or absence of a schema).<br> <br> This can be used by a system (eg a language model, a symbolic system, a neuro-symbolic approach) to identify image schemas. The file "language-models.csv" includes the results of two language models that were tested (T0pp, GPT-3).</p> <p>Metrics used to evaluate:<br> 1. Accuracy : no. correct predictions / no. of total sentences (TP + TN / P + N)<br> 2. Precision: no. correct image schema predictions / total correct image schema predictions (TP / TP + FP)<br> 3. Recall : : no. correct image schema predictions / total predicted image schema (TP / TP + FN)<br> 4. F1 : harmonic mean of Precision and Recall</p> <p>Code : https://github.com/kmitd/muhai-EPL </p>
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Data for "Ab initio studies of the Rg–NO+(X1Σ+) van der Waals complexes (Rg = He,Ne, Ar, Kr, and Xe)"
<p>This upload contains data files with tabulated values of Potential Energy surfaces calculated with CCSD(T)-F12 method for the Rg-NO(+)(r=re), where Rg=He, Ne, Ar, Kr and Xe published in the following paper: </p> <p>Citation: Cahit Orek, Jacek Kłos, François Lique and Niyazi Bulut, The Journal of Chemical Physics 144, 204303 (2016); <br> doi: 10.1063/1.4950813 <br> View online: http://dx.doi.org/10.1063/1.4950813</p> <p>Files:</p> <p>Readme_RgNOplus.txt<br> He-NO(+): HeNOplus_re_Eint.dat<br> Ne-NO(+): NeNOplus_re_Eint.dat<br> Ar-NO(+): ArNOplus_re_Eint.dat<br> Kr-NO(+): KrNOplus_re_Eint.dat<br> Xe-NO(+): XeNOplus_re_Eint.dat<br> Content of the files: <br> Column 1: r(NO)=re=2.0125 bohr<br> Column 2: Jacobi distance R in bohr describing distance of Rg from the center of mass of NO(+). Grid of ~40 points covers values from 3.5 or 4.5 for larger Rg to 30 bohr. <br> Column 3: Jacobi angle theta between r and R. Grid covers values from 0 to 180 degrees every 10 degrees.<br> Column 4: Interaction energy in cm-1, asymptotic value Eint=0 at infinite separation of Rg from NO(+)(r=re)</p>
Data and code used in manuscript: Basal freeze-on generates complex ice-sheet stratigraphy
<p>Mapped plumes location obtained from ice-sheet radio echo sounding data of North Greenland (https://data.cresis.ku.edu/data/rds/ for 2010-2014_Greenland files) and map of calculated freeze-on index are found in 'FreezeOnIndex_MappedPlume_Data.nc'. Model code of the three models used to obtain the findings shown in the manuscript 'Basal freeze-on generates complex ice-sheet stratigraphy'. As well as code to calculate the freeze-on index.</p>
Synthesis, Structure and Redox Properties of Single-atom Bridged Diuranium Complexes Supported by Aryloxides
<p>This upload contains raw data (NMR, X-Ray Diffraction, Electrochemistry, SQUID and Elemental Analysis) files for the article</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.