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12,625 results for “LINE”
Data for a publication "Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials"
<div> <p>These data are published as part of the paper: “Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials” published in journal: “Materials”. </p> </div> <div> <p>This repository contains one folder, namely: “concentration ICP_MS” </p> </div> <div> <p>This folder contains further data relevant to the results published in the paper, which are described in a separate file inside. </p> <p> </p> <p><strong>Preprint evolution (versions).</strong></p> <p><strong>2024-01-31-V2</strong>; <a href="https://doi.org/10.20944/preprints202401.2053.v2" target="_blank" rel="noopener">(https://doi.org/10.20944/preprints202401.2053.v2</a>) - the acknowledgement was modified as well as the data availability mentioning the Zenodo repository with the dataset as well as the availability of the datasets generated during and/or analyzed during the current study on reasonable request from corresponding author.</p> </div> <div> <p> </p> </div>
Unveiling Genomic Rearrangements in Engineered iPSC Lines
<p>Bionano smap files of Dual Analysis of each detected unique SV not present in parental cells. Only SVs within 12kbp of a canonical gene are presented for clarity.</p>
Dataset for "Degradable and Printed Microstrip Line for Chipless Temperature and Humidity Sensing"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Degradable and Printed Microstrip Line for Chipless Temperature and Humidity Sensing”. This work aims to study the humidity and temperature response of eco-friendly materials using a printed multi resonating microstrip line operating from 1.0 GHz to 2.6 GHz. The S12 signal of the resonator was measured using a vector network analyzer when varying the humidity inside a climatic chamber from 30% to 70% RH for temperatures of 15 °C, 25 °C and 35 °C. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
m6Am landscape of human cell lines
<p>Tables of m6Am profiling results by CROWN-seq. </p> <p>Cell lines in included in this version are:</p> <ol> <li>HEK293T (wild-type, PCIF1 KO, FTO KO)</li> <li>A549</li> <li>HepG2</li> <li>Huh-7</li> <li>CCD841 CoN</li> <li>HT-29</li> <li>HCT-116</li> <li>K562</li> <li>Jurkat E6.1</li> </ol> <p>ReCappable-seq data for HEK293T and A549 (WT and PCIF1 KO) are also included.</p> <p>Description of the table columns can be found in <code>README.md</code>.</p>
Distributional data for Lined Seedeaters (Sporophila lineola) and Saffron Finches (Sicalis flaveola)
<p><span>Distributional data for Lined Seedeaters (Sporophila lineola) and Saffron Finches (Sicalis flaveola). Data compiled from the literature, zoological collections, and community science platforms. </span></p>
Data from: Reproducibility of the Quantification of Reversible Wall Interactions in VOC Sampling Lines
<p>Dataset from the following publication (<a href="https://doi.org/10.3390/atmos12020280">https://doi.org/10.3390/atmos12020280</a>). In the paper, a method to quantify the amount of substance segregated by reversible interactions on sampling lines is proposed. The areic amount of a VOC (Acetone) interacting with the pipe is measured for a commercial test pipe (Sulfinert®) as the amount of substance per unit area of the internal surface of the test pipe segregated from the flowing gas mixture. The areic amount is function of numerical integrals estimated under different conditions and reproducibility is evaluated. The data used to estimate the integrals described in this work is organised in folders. Each folder correspond to a sample. Sample information is available on Table 3 of the paper.</p>
Spatially gridded cross-shelf hydrographic sections and monthly climatologies from shipboard survey data collected along the Newport Hydrographic Line, 1997-2021
<p>This data set, described in detail in <a href="https://www.sciencedirect.com/science/article/pii/S2352340922001342">Risien et al. (2022)</a>, contains Newport Hydrographic Line station data; gridded, cross-shelf hydrographic sections; and derived monthly climatologies for temperature, practical salinity, potential density, spiciness, and dissolved oxygen. It consists of CSV (Comma Separated Values) files (<em>newport_hydrographic_line_station_data</em><em>.</em><em>zip</em>) that contain CTD observations collected at the seven hydrographic stations located 1, 3, 5, 10, 15, 20 and 25 nautical miles west of Newport, Oregon between March 1997 and July 2021. Additionally, the data set contains three NetCDF files that follow CF (Climate and Forecast) metadata conventions: <em>newport_hydrographic_line_gridded_sections</em><em>.nc</em> contains observations gridded to a 0.01<sup>o</sup> x 1 dbar longitude - pressure grid to create cross-shelf hydrographic sections for each of the five variables for each cruise. <em>newport_hydrographic_line_gridded_section_climatologies</em><em>.nc</em> contains climatological hydrographic sections, calculated using harmonic analysis over the 24-year period March 1997 to February 2021 and reported here for the middle of each month, and <em>newport_hydrographic_line_gridded_section_coefficients.nc</em> contains the associated linear regression model coefficients for all five variables. From the regression coefficients, users can construct seasonal cycles at any location in the gridded section with a temporal resolution that best suits their specific needs. Finally, this data set includes example MATLAB and R scripts that show how to read the data files, plot cross-shelf hydrographic sections, and calculate daily and monthly climatologies using the regression coefficients.</p>
Reconstruction of full-length LINE-1 progenitors from ancestral genomes (Supplementary Data)
<p><strong>Web Supplementary Files</strong></p> <ul> <li>Web Supplementary File 1 - FASTA files containing full-length reconstruction input sequences<strong>: full_length_reconstruction_input_sequence_fastas.zip</strong></li> <li>Web Supplementary File 2 - FASTA files containing Muscle alignments of the full-length reconstruction input sequences.<strong> full_length_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 3 - FASTA file of full-length reconstructed sequences:<strong> full_length_reconstructions.fa</strong></li> <li>Web Supplementary File 4 - Table of full-length reconstruction statistics: <strong>full_length_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 5 - FASTA files containing ORF reconstruction input sequences:<strong> orf_fastas.zip</strong></li> <li>Web Supplementary File 6 - FASTA files containing Macse alignments of the ORF reconstruction input sequences:<strong> ORF_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 7 - Table of ORF reconstruction statistics: <strong>ORF_reconstructions.fa</strong></li> <li>Web Supplementary File 8 - Table of ORF reconstruction statistics: <strong>ORF_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 9 - Table of Composite Sequences: <strong>bestfl_selection_fixed_CS_seqs.csv</strong></li> <li>Web Supplementary File 10 - Database of gold standards: <strong>L1_goldstandards.csv</strong></li> </ul> <p><strong>Data Underlying Figures</strong></p> <ul> <li>RepeatMasker scans of hg38 and ancestral genomes:<strong> </strong><strong>anc_gen_RM_out_files.zip</strong></li> <li><strong>Figure 4</strong> <ul> <li>4A <ul> <li>Source alignment of 54 composite sequences: <strong>220121_dropped12+L1ME3A_muscle.nt.afa</strong></li> <li>Tree produced using the alignment and FastTree: <strong>220121_dropped12+L1ME3A.tree</strong></li> </ul> </li> <li>4B <ul> <li>Source alignment of 67 Dfam L1 subfamily 3’ end models: <strong>200123_dfam_3ends.fa.muscle.aln</strong></li> <li>Tree produced using the alignment: <strong>200123_dfam_3ends.fa.muscle.aln.tree</strong></li> </ul> </li> </ul> </li> <li><strong>Figure 5</strong> <ul> <li>KZFP-TE enrichment p-values (from Barazandeh <em>et al</em> 2018):<strong> TE_KZFP_enrichment_pvals.xlsx</strong></li> <li>KZFP-TE top 500 peak overlap (from Barazandeh <em>et al</em> 2018): <strong>top500_peak_overlap.xlsx</strong></li> </ul> </li> <li><strong>Figure 6</strong> <ul> <li>RepeatMasker .out file for the Composite Sequence custom library queried against hg38: <strong>CS_RM_hg38.fa.out.gz</strong></li> </ul> </li> <li><strong>Figure S2</strong> <ul> <li>RepeatMasker scan .out file of hg38 (CG corrected Kimura Divergence values are in last column): <strong>hg38+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Progressive Cactus eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Progressive_Cactus_Euth+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Ancestors 1.1 eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Ancestors_Euth+KimDiv_RM.out</strong></li> </ul> </li> <li><strong>Figure S5</strong> <ul> <li>RepeatMasker scan .out files for Progressive Cactus simian and primate reconstructed ancestral genomes: <strong>progCactus_RM_outfiles.zip</strong></li> <li>S5A <ul> <li>FASTA files containing Cactus genome-derived reconstructed sequences equivalent to the L1MA2, L1MA4, and L1MD1-3 best full-length sequences: <strong>progCactus_reconstruction_bestFL_equivalents.zip</strong></li> </ul> </li> <li>S5B <ul> <li>FASTA files containing Muscle alignments of Cactus genome-derived full-length reconstruction input sequences: <strong>progCactus_reconstruction_input_sequence_alns.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S6</strong> <ul> <li>S6A <ul> <li>Results of Conserved Domain scans of Cactus genome-derived full-length reconstructed sequences: <strong>CD_search_results_short_nms.txt</strong></li> </ul> </li> <li>S6B-D <ul> <li>Character posterior probabilities of “best” full-length reconstructed sequences: <strong>best_fl_post_probs.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S7</strong> <ul> <li>S7B-C <ul> <li>Results of Conserved Domain scans of translated initial full-length reconstructed sequences: <strong>initial_recons_all_3frametrans_CD-search.txt</strong></li> <li>Results of Conserved Domain scans of translated reconstructed ORFs: <strong>recons_ORF1-2_all_3frametrans_CD-search.csv</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S15</strong> <ul> <li>S15A <ul> <li>Source alignment of 67 composite sequences: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa</strong></li> <li>Tree produced using the alignment: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa.tree</strong></li> </ul> </li> <li>S15B-E <ul> <li>Source Muscle alignments for phylogenetic trees of reconstructed sequence components: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa</strong></li> </ul> </li> <li>Trees produced using above alignments: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa.tree</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa.tree</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa.tree</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa.tree</strong></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Figure S17</strong> <ul> <li>Unfiltered BLAST results of Composite Sequences queried against hg38: <strong>CS_hg38_blastn.csv.zip</strong></li> <li>BED file of L1 instances annotated using BLAST pipeline: <strong>BLAST_L1_hits.bed</strong></li> </ul> </li> </ul>
Lightning flashover simulations on medium voltage distribution lines
<p>[Version 1.2] This version of the dataset fixes a bug found in the previous versions (see below for more information).</p> <p>Dataset has been generated from the <strong>Monte Carlo</strong> simulations of <strong>lightning flashovers</strong> on medium voltage (MV) <strong>distribution lines</strong>. It is suitable for training <strong>machine learning</strong> models for classifying lightning flashovers on distribution lines, as well as for line insulation coordination studies. The dataset is hierarchical in nature (see below for more information) and class imbalanced.</p> <p>Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.</p> <p><strong>Dataset</strong> consists of the following variables:</p> <ul> <li>'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,</li> <li>'shield': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,</li> <li>'Ri': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'EGM': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],</li> <li>'CFO': critical flashover voltage level of the distribution line's insulation (kV); following three levels have been used: 150, 150, and 160 kV, respectively, for three different distribution lines of height 10, 12, and 14 m,</li> <li>'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, and 14 m; twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart; data set consists of 10000 simulations for each line height,</li> <li>'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).</li> </ul> <p><strong>Note</strong>: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV for the first two lines (10 m and 12 m) and 160 kV for the third line (14 m), and that the diameters of the phase conductors and shield wires for all treated lines are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm for all treated cases (it has no discernible influence on the flashover rate). Dataset is class imbalanced and consists in total of 30000 simulations, with 10000 simulations for each of the three different MV distribution line heights (geometry) and CFO levels.</p> <p><strong>Important</strong>: Version 1.2 of the dataset fixes an important bug found in the previous data sets, where the column 'Ri' contained duplicate data from the column 'veloc'. This issue is now resolved.</p> <p>Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below.</p> <p><strong>References</strong>:</p> <p>J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005, doi: 10.1109/TPWRD.2005.848734.</p> <p>A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.</p>
Water conformation at three-phases contact lines
<p>This dataset contains detailed trajectories obtained from molecular dynamics simulations performed using GROMACS. The simulated system consists in a quasi-2-dimensional SPC/E water meniscus confined between solid substrates composed by silica quadrupoles. Details on the molecular model and the simulation technique can be found in the references article. The data collected in this dataset are utilized in a ongoing project aimed to demystify the motion of three-phases contact lines over hydrophilic surfaces.</p>
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
Airborne EM data (Belgium) from flight line 306025
<p>This dataset contains Airborne EM data from a SkyTEM instrument from the Flanders region, Belgium. </p> <p>Details about the instrument set-up can be found in the data report.</p> <p>Details about the region, geology, saltwater intrusion context can be found in </p> <p>Delsman, J., van Baaren, E., Vermaas, T., Karaoulis, M., Bootsma, H., de Louw, P. G. B., ... & Thofte, S. (2019). TOPSOIL Airborne EM kartering van zoet en zout grondwater in Vlaanderen (FRESHEM Vlaanderen: Deelopdrachten 1 tot en met 3.</p> <p><strong>When using this dataset, always cite the above reference. </strong></p> <p>The actual measured data is in "dat_skytem_306025_flightline.csv". Columns refer to either Low or High moment and time of measurement. Corresponding estimated relative errors can be found in rel_err_skytem_306025_flightline. Distances between soundings/measurment locations and their hieghts above the surface is found in "distances_between_soundings" and "altitudes_per_sounding" respectively.</p> <p>To use this data for the appraisal method, see https://github.com/WouterDls/AEM_appraisal. For more information, write to wouter.deleersnyder@kuleuven.be </p>
implementation of an in-line Kerr active cavity equipped with a loop mirror
<p>This dataset includes the measurements of the resonances collected at the through port of an active fiber cavity (in in-line configuration) of 5 meters of length based on a step index silica fiber and including a loop mirror and a fiberized mirror at the two ends of the cavity. The dataset includes also the measurement of the effective losses of the in-line active cavity vs. the intracavity power. </p>
Variable Optical True Time Delay Line Breaking Bandwidth-Delay Constraints - Dataset
<p>Dataset for the Letter "Variable Optical True Time Delay Line Breaking Bandwidth-Delay Constraints", in Optics Letters</p>
Impact of paclitaxel treatment on the Triple Negative Breast Cancer Cell line HCC1143
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11237850</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate the impact of escalating paclitaxel dose on cell count, nuclear morphology and cellular outcome.</div> <div> </div> <div>Methods:</div> <div>Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were blocked with 1% BSA (A7906-100G, Millipore Sigma) in PBS for 1 hour at room temperature and then stained overnight with 1:100 anti-CDKN2A/p16INK4A+CDKN2B/p15INK4B-AF644 (#ab199756, Abcam), and 1:100 anti-cPARP-AF647 (#6987S, Cell Signaling Technology) or 1:500 anti-TUBB3-AF647 (#ab190575, Abcam) overnight at 4C. Each well was washed twice with room temp PBS then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask in PBS (Orange: #H32713, Green: #H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 45, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>Included files:</div> <div>row_#_level_1.zip : 6 zip file containing original images from InCell 6000, one zip per row</div> <div>level_2.csv : Data quantified to the nuclear level (cytoplasmic quantification is duplicates across multiplet nuclei)</div> <div>level_3.csv: Data quantified at the cellular level including number of nuclei and stain intensities for segmented compartments</div> <div>platemap.csv: Description of each well from the stained plate</div> <div>cellpose_modelz.zip: Zip file containing the two CellPose models used for segmentation</div> <div>image_quantification.rmd : R markdown file containing code for extracting and quantifying image intensities using the raw images (level_1) and segmentation masks created from cellpose.</div>
The Monk Line Segmentation (MLS) Dataset
<p>Overview</p> <p>The MLS dataset available from this page consists of 31 handwritten page scans. The dataset contains medieval, historical and contemporary manuscripts, and has the purpose of testing line-segmentation algorithms. The collection contains a wide variation of the common problems in handwriting recognition: lines with overlapping ascenders/descenders, slightly rotated scans and curved base lines. <br> </p> <p>Download</p> <p>The MLS dataset was collected from the Monk system as of Friday May 17 14:15:04 CEST 2013. It was collected by Lambert Schomaker in May 2013 at the Institution of Artificial Intelligence and Cognitive Engineering (ALICE), University of Gronigen. </p> <p>The tar.gz file contains the image dataset for historical manuscripts. For more details please refer to the README file in the tar.gz file. The dataset downloaded for research use only. © 2013 Copyright. <br> </p> <p>@INPROCEEDINGS{Surinta:2014:ICFHR,<br> author = {O. Surinta and M. Holtkamp and M. F. Karaaba and JP. van Oosten and L. R. B. Schomaker and M. A. Wiering},<br> title = {A* Path Planning for Line Segmentation of Handwritten Documents},<br> booktitle = {Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on},<br> year = {2014},<br> month = {Sep},<br> pages = {175-180},<br> numpages = {6},<br> isbn = {978-1-4799-4335-7},<br> issn = {2167-6445},<br> publisher = {IEEE},<br> doi = {http://dx.doi.org/10.1109/ICFHR.2014.37},<br> }</p>
Electron Bessel beam diffraction patterns, line scan of Si/SiGe multilayer
<p>Electron diffraction patterns taken with a conical illumination (electron Bessel beams) and can be used to measure strain.</p> <p>The experimental diffraction patterns, in DM3 format, are included in the file experimental_data.7z while FEM strain simulations for the same sample are in the file reference_strain_experimental.csv</p> <p>Simulated diffraction patterns are included in the file simulated_patterns.7z while the strain in the model used is in the file reference_strain_simulated_patterns.csv</p> <p> </p> <p>The two python scripts attached allow the extraction of the strain, and rely on the code published at:</p> <p>https://bitbucket.org/lutosensis/tem-thesis/</p> <p> </p>
Mulit-Criteria Power Line Routeing GIS_dataset 2
<p>The dataset presented here contains GIS (Geographic Information System) data relevant for infrastructure projects, specifically development of DC grid.</p> <p>The data has been sourced from a range of sources and proceeded to be compatible for combination and analysis. For each data set included, the conversion of the original data into a 500m grid in EPSG 31468 projection with the raster calculator implemented in QGIS has been carried out.</p> <ul> <li>Elevation</li> </ul> <p>The elevation data originate from the EEA [1]. The resolution of the original data corresponds to about 25m and is available in raster format. The bilinear resampling method was used to determine the values of the new grid fields.</p> <ul> <li>Landscape quality assessment</li> </ul> <p>Uniform assessment per grid field between 0 (low) and 10 (high) derived from the original data set of [2] with a total of information on 44 primary land use types based on the assessment criteria of [3]. The data set used here is the CLC2012 which refers to the reference year 2012. </p> <ul> <li>Population density</li> </ul> <p>Source of population density is a raster grid provided by the [4] which expresses the number of people per pixel with a resolution of 250m x 250m. The base year for population data is 2015.</p> <ul> <li>Protected areas</li> </ul> <p>Reduction of data from [5] to continental Europe and change of projection.</p> <ul> <li>Slope</li> </ul> <p>Derived from elevation data based on [1]. The slope is calculated by the tilt angle for each raster cell in degrees based on the first-order derivation. </p> <p> </p>
[Dataset] Software Process Line as an Approach to Support Software Process Reuse: a Systematic Literature Review
<p>Dataset of a Systematic Literature Review on Software Process Line as an Approach to Support Software Process Reuse</p> <p>Please, read README.txt file before go through dataset.</p>
A Dataset for In-situ synchrotron tomography experiments to investigate anisotropic damage of line pipe steel
<p>In this study, anisotropic ductility and associated damage mechanisms of a grade X100 line pipe steel were investigated using in-situ synchrotron-radiation computed tomography (SRCT) of notched round bars. Line pipe materials have anisotropic mechanical properties, such as tensile strength, ductility and toughness. Specimens were tested for loading along both rolling (L) and transverse (T) directions. The <em>in-situ</em> data collected allowed quantifying both specimen deformation (evolution of the cross section) and microscopic damage parameters such as porosity, void shape and void orientation. The data sets provide here are related to the paper <em>"On the origin of the anisotropic damage of X100 line pipe steel, Part I: in-situ synchrotron tomography experiments"</em> being published in <a href="https://www.springer.com/journal/40192">Integrating Materials and Manufacturing Innovation</a>. For each testing direction, dataset are provided using hdf5 and xdmf standarded exchange format. A compressed file is also provided in connection with the analyses explained in the article.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.