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1,081 results for “Data publication”
Data and code for the publication "The effect of rainfall changes on economic production"
<p>The zipped file of this repository contains code and data to reproduce the results of the publication:</p> <p>Kotz et al, Nature, The effect of rainfall changes on economic production. (2021). <a href="http://doi.org/10.1038/s41586-021-04283-8">https://doi.org/10.1038/s41586-021-04283-8</a></p> <p> </p> <p>Economic data are derived from the DoSE database: <a href="http://doi.org/10.5281/zenodo.4681305">https://doi.org/10.5281/zenodo.4681305</a></p> <p>Climate data are derived from the ERA-5 reanalysis: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 </a></p> <p> </p> <p>Please see the README document for detailed:</p> <p>- Descriptions of the code and data provided</p> <p>- Lists of the required dependencies</p> <p>- Naming conventions for variables</p> <p> </p>
Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples
<p># Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples"</p> <p>SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes.</p> <p>SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results.</p> <p>SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID.</p> <p>SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows:<br> * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020.<br> * rab.all: median clr value for all samples in the feature<br> * rab.win.conditionA: median clr value for the condition A of samples<br> * rab.win.conditionB: median clr value for the condition B of samples<br> * diff.btw: median difference in clr values between A and B conditions<br> * diff.win: median of the largest difference in clr values within A and B conditions<br> * effect : median effect size: diff.btw / max(diff.win) for all instances<br> * overlap : proportion of effect size that overlaps 0 (i.e. no effect)<br> * we.ep: Expected P value of Welch’s t test<br> * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test<br> * wi.ep: Expected P value of Wilcoxon rank test<br> * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test<br> * parts: gene name<br> * conditionA: label of condition A that is compared against condition B<br> * conditionB: label of condition B that is compared against condition A<br> * conditions.A.vs.B: label to explain condition A compared against condition B<br> NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs</p> <p>SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences.</p> <p>SD6: FASTA file containing all unique consensus sequences reported in the manuscript.</p> <p>SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.</p>
Data and ancillary data for publication: Natural infrastructure and water erosion mitigation in the Andes
<p>The data contain information on the effectiveness of natural infrastructure to mitigate soil erosion. Data were compiled from 118 case studies from the Andean region, whereby information on natural infrastructure interventions, soil erosion and soil quality were tabulated and analysed.</p> <p>The data contains the following documents:<br> -Database with data on soil erosion, soil quality for different types of natural infrastructure (118 case studies)<br> -Metadata<br> -Summary of terms used in the systematic review of the literature (in Spanish and English)<br> -List of bibliographic data sources that were searched with the search terms<br> -Full bibliographic references of all 118 case studies</p> <p><strong>Full reference </strong></p> <p><em>Vanacker V, Molina A, Rosas-Barturen M, Bonnesoeur V, Román-Dañobeytia F, Ochoa-Tocachi B, Buytaert W (2022). The effect of natural infrastructure on water erosion mitigation in the Andes. </em></p> <p> </p> <p> </p>
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.</p>
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Data from: Public human microbiome data are dominated by highly developed countries
<p>Supplementary tables and datasets associated with the <em>PLOS Biology </em>publication "Public human microbiome data are dominated by highly developed countries." See README.txt for a description of the files and the data fields they contain.</p> <p>Update, 8 Apr 2022: The "figures.md" file described in the readme was inadvertently left out of the initial upload. It has been added here.</p>
Publication and Maintenance of Relational Data in Enterprise Knowledge Graphs Created (Files used in the experiments)
<p>This dataset contains two files created for the experiments presented in the article: Publication and Maintenance of RDB2RDF Views Externally Materialized in Enterprise Knowledge Graphs.</p> <p><strong>mapR2RML_MusicBrainz_completo.txt</strong><strong>:</strong> We created the R2RML mapping for translating MBD data into the Music Ontology vocabulary, which is used for publishing the LMB view. The LMB view was materialized using the D2RQ tool. It took 67 minutes to materialize the view with approximately 41.1 GB of NTriples. We also provided SPARQL endpoint for querying LMB View.</p> <p><strong>TriggersAndProcedures.txt</strong>: We created the triggers, procedures, and class in java to implement the rules required to compute and publish the changesets.</p> <p><strong>relationalViewDefinition.pdf</strong>: This document gives details about the process of creating the relational views used in the experiments.</p>
Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces'
<p>Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces' by Kuppusamy Senthil Kumar et al. The two versions of Fig. 4d datasets correspond to the preprint version (https://zenodo.org/record/4905692#.Ymj9odpBxaQ) and publication version (https://www.nature.com/articles/s41467-021-22383-x), since a new set of data was taken during the review process. </p>
Data and code for the publication "DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis"
<p>The zipped file of this repository contains code and data to reproduce the results of the publication:</p> <p>Zhao et al, DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis. Genome Biology (2022). </p> <p>All sequence data have been deposited in NCBI GEO accession codes GSE183987 and GSE169497.</p> <p> </p> <p>Please see the README document for detailed:</p> <p>- Descriptions of the code and data provided</p> <p>- Lists of the required dependencies</p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".
<p>This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.</p> <p> </p>
Figure Sets and Data Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M"
<p>Images for Figure Sets 4, 5, 6, 7, and 9 and data behind the figure for Figure 15 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M" (Currently accepted and in press.). Figure sets and file names are described in the fsREADME file. Data behind the figure is described in the dbfREADME file.</p>
Data repository for the publication "Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern"
<p>This repository contains the data and scripts associated with the article “Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern“, written by Jessica Coria, Erik Kristiansson and Mikael Gustavsson.</p>
Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"
<p>Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing.</p> <p>The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab.</p> <p>This work is available in medRxiv and has been submitted</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Simulated data for MetaGT publication
<p>The datasets contains metagenomic and metatranscriptomic Illumina reads simulated for 3 different sets of Bacteria:</p> <p>- Simple7<br> 7 bacterial species, inlcuding 2 members of the genus Corynebacterium.<br> - Medium20<br> 20 bacterial species, including 2 members of the genus Corynebacterium.<br> - Complex32<br> 32 bacterial species, including 2 strains Escherichia coli, 3 members of the genus Shigella,2 members of the Salmonella, 4 members of the Lactobacillus, 2 members of the Corynebacterium, 2 members of the Desulfosporosinus.</p>
Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.
<p>These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip.</p> <p>Links:</p> <p>Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks</p> <p>Poster: https://zenodo.org/record/1134154</p> <p>Code: https://github.com/fouticus/pipgcn</p> <p> </p> <p><strong>File Descriptions:</strong></p> <p>train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against. </p> <p>train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> <p>test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes. </p> <p>Descriptions of the vertex and edge features can be found in Appendix A of <a href="https://mountainscholar.org/handle/10217/185661">this.</a></p> <p>diffc_g2_p2_train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> <p>diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes.</p> <p>diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2. </p> <p> </p> <p>Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'. </p> <p> </p> <p>Please direct any questions to:</p> <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>
Data for a publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance"
<p>The data set for contains data used in the article "LIPSS pattern induced by polymer surface instability for myoblast cell guidance".</p> <p><strong>Versions:</strong></p> <p><em><strong>V2: </strong>The current version contains reorganized file folders and is supplemented by the data found in the publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance". The dataset newly contains contact angle, cytocompatibility tests, FTIR analyses or Zeta potential results.</em></p> <p><strong>Article abstract</strong></p> <p>The presented study highlights the efficiency of employing a KrF excimer laser to create diverse types of periodic nanostructures (LIPSS – laser induced periodic surface structures) on polyether ether ketone (PEEK) and polyethylene naphthalate (PEN) substrates. LIPSS structures are very important both in tissue engineering and find also strong application in the field of sensor construction, and SERS analysis. By exposing the polymer films below their ablation threshold to laser fluence ranging from 4 to 16 mJ·cm<sup>-2</sup> at 6,000 pulses, we studied both single-phase exposure at beam incidence angles of 0° and 45°, and two-phase exposure. Atomic force microscopy analysis revealed that the laser-treated samples contained distinctive periodic patterns such as waves, globules, and pod-like structures each exhibiting unique surface roughness. Moreover, using analytical methods like EDS and XPS shed light on the changes in the atomic composition, specifically focusing on the C and O elements, as a result of laser exposure. Notably, in almost all cases, we observed an increase in oxygen percentage on the sample surfaces. This increase not only led to a decrease in the contact angle with water but also lowered the zeta potential value, thus showing that the modified samples have enhanced hydrophilicity of the surface and altered electrostatic properties. Last but not least, the samples were assessed for biocompatibility; we studied the interaction of the prepared replicates with mouse myoblasts (C2C12). The impact of globular/dot structures on the cell growth in comparison to pristine or linear LIPSS-patterned surfaces was determined. The linear pattern (LIPSS) induced the myoblast cell alignment along the pattern direction, while dot/globular pattern even enhanced the cytocompatibility compared to LIPSS samples. Through this comprehensive analysis, the research underscores the multifaceted implications of employing KrF excimer laser-induced nanostructures, ranging from surface morphology alterations to biocompatibility enhancements, thus, opening new avenues for advanced material engineering.</p>
Improving access to and reuse of research results, publications and data for scientific purposes - Stakeholders' consultations results
<p>The data sets were created via data collection effort for the Horizon Europe-funded "study to evaluate the effects of the EU copyright framework on research and the effects of potential interventions and to identify and present relevant provisions for research in EU data and digital legislation, with a focus on rights and obligations". The study was contacted by DG RTD. </p> <p>This research project supports Action 2 objectives of the European Research Area (ERA) Policy Agenda 2022-2024, which aims to propose an EU legislative and regulatory framework for copyright and data that is fit for research. The report provides a comprehensive analysis of barriers to the access and reuse of publicly funded research, including scientific publications and data. It assesses existing EU copyright legislation and EU data and digital legislation. It also assesses regulatory frameworks and national initiatives and identifies potential areas for improvement.</p> <p>Using a methodological, evidence-based approach (including the survey results posted in this repository), the study presents possible legislative and non-legislative measures to improve the current EU copyright and data framework and align it with the needs of scientific research and open research data principles. </p> <p>The data sets include the raw data of the three surveys (survey 1 targeted at researchers, survey 2 targeted at research-performing organisations, and survey 3 targeted at publishers). All surveys have two major parts: one concerning copyright legislation and another concerning data and digital legislation. In addition, we provide interview notes, they are also organised into two parts: one concerning copyright legislation and another concerning data and digital legislation. </p> <p>The data collection effort was partially supported by our colleagues from the Institute for Information Law (IVIR) and KU Leuven CiTIP. </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.