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6,704 results for “reports”
Fig. 1 in A report of 36 unrecorded bacterial species belonging to the phyla Actinomycetota, Bacillota, Bacteroidota, Deinococcota, and Pseudomonadota isolated in Republic of Korea
Fig. 1. Neighbor-joining phylogenetic tree based on 16S rRNA gene sequences showing the relationships between 19 strains isolated in this study and their relatives in the phyla Actinomycetota, Bacillota, Bacteroidota and Deinococcota. Escherichia coli ATCC 11775T (GenBank accession no. X80725) was used as an outgroup. Bootstrap values greater than 70% are shown at branching points. Bar, 0.05 substitutions per nucleotide position.
Fig. 1 in A report of 11 unrecorded bacterial species in Korea, isolated from Hapcheonho Lake and Jinyangho Lake
Fig. 1. Neighbor-joining tree based on 16S rRNA gene sequences showing the phylogenetic relationships among the eleven isolates and their closest relatives. Filled circles indicate the nodes that were also recovered in the maximum-likelihood tree. Bootstrap values are shown above nodes.
Fig. 1 in A report of six unrecorded radiation-resistant bacterial species isolated from soil in Korea in 2018
Fig. 1. Transmission electron micrographs of the strains isolated in this study. Strains: 1, 18JY42-3; 2, 18SH; 3, 18JY76-11; 4, 17J11-11; 5, 18JY14-14; 6, 18JY15-11.
Fig. 4 in A report of 29 unrecorded bacterial species belonging to the phylum Bacteroidetes in Korea
Fig. 4. Neighbor-joining phylogenetic tree based on 16S rRNA gene sequences, showing the phylogenetic relationships between the strains isolated in this study and their relatives of the order Sphingobacteriales in the phylum Bacteroidetes. Escherichia coli ATCC 11775T (X80725) was used as an outgroup (not shown). Bootstrap values (>70%) are shown above nodes. Scale bar: 0.01 changes per nucleotide.
Fig. 1 in A report of 29 unrecorded bacterial species belonging to the phylum Bacteroidetes in Korea
Fig. 1. Transmission electron micrographs or scanning electron micrographs of cells of the strains isolated in this study. The cells were cultured at their optimal growth conditions. Strains: 1, HN30-2; 2, RDH3; 3, G4; 4, HMF3809; 5, YBF1; 6, JD36; 7, MW24; 8, LPB0108; 9, M2015-5; 10, KYW1172; 11, C2-40; 12, NR 2-02; 13, DF-2; 14, M2015-1; 15, RDH2; 16, HMF4059; 17, 2015-2; 18, HN41; 19, HN1; 20, HN49; 21, KYW1047; 22, MW105; 23, HMF2508; 24, NU3; 25, HMF3898; 26, LPB0111; 27, HMF3876; 28, LPB0115; 29, HMF3932.
ufo-reports
<p>NUFORC geolocated and time standardized ufo reports for close to a century of data. 80,000 plus reports.</p>
OTU table - Joli et al. Scientific Reports - Janus Gateway
<p>This texte file represent the OTU table from the paper Joli et al. published in Scientific Reports as "Need for focus on microbial species following ice melt and changing freshwater regimes in a Janus Arctic Gateway". The first column contains the OTU number and each following columns contain the number of reads for each sample. The last column gives the taxonomy after confronting dataset to a eukaryotic 18S database.</p>
Material related to the blog that reports on the parrot LUT
<p><strong>Material related to the blog that reports on the parrot LUT</strong></p> <p>The blog was published at the Node: <a href="http://thenode.biologists.com/parrot-lut/research/">http://thenode.biologists.com/parrot-lut/research/</a></p> <p> </p> <p><strong>-Source</strong></p> <p>The ‘morgenstemning’ LUT was originally described in:</p> <p>M. Geissbuehler and T. Lasser - "How to display data by color schemes compatible with red-green color perception deficiencies”, Optics Express, 2013</p> <p>The ‘inferno’ LUT was originally created by Stéfan van der Walt and Nathaniel Smith (<a href="http://bids.github.io/colormap/">http://bids.github.io/colormap/</a>).</p> <p>The ‘pseudocolorMM’ LUT was derived from MetaMorph software (version 7.6).</p> <p>The ‘royal’ and ‘Fire’ LUT are available in ImageJ (version 1.49j)</p> <p>The ‘parrot’ LUT was designed by Joachim Goedhart and first described here:<br> <a href="http://thenode.biologists.com/parrot-lut/research/">http://thenode.biologists.com/parrot-lut/research/</a></p> <p><br> <strong>-Distribution</strong></p> <p>The colormaps Magma, Inferno, Plasma and Viridis are available under a CC0 "no rights reserved" license (<a href="https://creativecommons.org/share-your-work/public-domain/cc0">https://creativecommons.org/share-your-work/public-domain/cc0</a>) and are present in FIJI.</p> <p><br> The colormaps Mongenstemning & Parrot are free software: you can redistribute them and/or modify<br> it under the terms of the GNU General Public License as published by<br> the Free Software Foundation, either version 3 of the License, or<br> (at your option) any later version.</p> <p>These colormaps are distributed in the hope that they will be useful,<br> but WITHOUT ANY WARRANTY; without even the implied warranty of<br> MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the<br> GNU General Public License for more details: <<a href="http://www.gnu.org/licenses/">http://www.gnu.org/licenses/</a>>.</p>
Report of the Posidonia data set done at the CIEM wave flume on 2008
<p>The data set presents the results from experiments done in the CIEM large wave flume of Barcelona on wave and flow attenuation by a full-scale artificial Posidonia oceanica seagrass meadow in shallow water conditions. </p> <p>More information can be found on the published papers:</p> <p>Manca, E., I. Caceres, J. Alsina, V. Stratigaki, I. Townend, C.L. Amos., 2012. Wave energy and wave-induced flow reduction by full-scale model Posidonia oceanica seagrass. Continental Shelf Research, Vol. 50-51 ,pp. 100 - 116.</p> <p>Stratigaki, V., Manca, E., Prinos, P., Losada, I., Lara, J., Sclavo, M., Amos, C., Cáceres, I. and Sánchez-Arcilla, A., 2011. Large-scale experiments on wave propagation over Posidonia oceanica. Journal of Hydraulic Research, Vol. 49, pp. 31-43.</p> <p> </p>
Data providers package for reporting Chemical Contaminants (official data reporting phase) SSD2
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (<a href="http://registerofquestions.efsa.europa.eu/roqFrontend/mandateLoader?mandate=M-2010-0374">M-2010-0374</a>) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.</p> <p>This data providers package provides the data collection configuration and supporting materials for reporting <strong>Chemical Contaminants in SSD2</strong>. These are to be used for the official data reporting phase.</p> <p>The package includes:</p> <p>CHECK advice on the values to be reported for the mandatory fields specified for this data collection.</p> <p>The Standard Sample Description Version 2 XSD schema definition for CONTAMINANTS reporting.</p> <p>The STX transformation file which automatically assigns sampEventId and sampAnId when this information is not provided.</p> <p>The general and CONTAMINANTS SSD2 specific business rules applied for the automatic validation of the submitted datasets.</p> <p>Excel Mapping tool to convert excel files after mapping into XML document.</p> <p>Guidance on how to use the Excel Mapping tool.</p> <p>Guidance on how to run the validation report after submitting data to the DCF.</p>
Data providers package for reporting Chemical Contaminants (official data reporting phase) SSD1
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (<a href="http://registerofquestions.efsa.europa.eu/roqFrontend/mandateLoader?mandate=M-2010-0374">M-2010-0374</a>) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.</p> <p>This data providers package provides the data collection configuration and supporting materials for reporting <strong>Chemical Contaminants in SSD1</strong>. These are to be used for the official data reporting phase.</p> <p>The package includes:</p> <p>The Standard Sample Description Version 2 XSD schema definition for CONTAMINANTS reporting.</p> <p>The general and CONTAMINANTS SSD1 specific business rules applied for the automatic validation of the submitted datasets.</p> <p>Excel Mapping tool to convert excel files after mapping into XML document.</p> <p>Please follow the instructions below for the correct use of the mapping tool to avoid compromising its functionalities:</p> <ol> <li>Download and save the MS Excel® Standard Sample Description file to your computer (do not open the file before saving and do not change the file name)</li> <li>Download and save the file MS Excel® Simplified Reporting Format (do not open the file before saving)</li> <li>Keep both Excel files in the same folder</li> <li>Open both Excel files and enable the macros</li> <li>Keep both files open in the same Excel instance when filling in the data</li> </ol> <p>Guidance on how to run the validation report after submitting data to the DCF.</p>
Scholix Report: Who Contributes to Scholix?
<p>This dataset contains the analysis of Scholix JSON Dump (DOI: <a href="https://doi.org/10.5281/zenodo.1200253">10.5281/zenodo.1200253</a>). The aim is to discover which journals contributing to Scholix Hubs. The scope of this analysis is limited to:</p> <ul> <li>OpenAIRE ScholeXplorer Service: Scholix JSON Dump (March 16, 2018) [DOI: <a href="https://doi.org/10.5281/zenodo.1200253">10.5281/zenodo.1200253</a>]</li> <li>Only included DOI from Crossref and excluded DataCite DOIs.</li> </ul> <p>This dataset includes three files:</p> <ol> <li>scholix-crossref-doi-metadata.tar.gz: Complete Crossref metadata for DOIs in Scholix JSON Dump resolved using https://api.crossref.org/works/{doi}</li> <li>scholix-crossref-doi-prefixes.csv: List of DOI prefixes extracted from scholix-crossref-doi-metadata.json</li> <li>scholix-journals-report.csv: Aggregated record from the number of unique DOIs per journal. This analytics is also derived from scholix-crossref-doi-metadata.json</li> </ol>
CATCH-EyoU: D6.2 School National Reports
<p>This is a cross-national dataset (Portugal, Italy, Sweden, Germany, Czech Republic and Estonia) containing data from interviews with teachers regarding youth active citizenship and EU and focus group discussions with students involved in associations/projects/activities that foster active citizenship.</p> <p>The data set integrates the perspectives on youth active citizenship and EU of teachers (in total, 101 interviews) and students (in total, 51 focus groups), which included the participants’ comments on the main results of the textbooks’ analyses conducted in the previous phase.</p> <p>The aim of the data set is to explore similarities and differences across participating countries regarding the EU and youth active citizenship at EU, national and regional level depicted in school curricula, school textbooks and among teachers and students.</p> <p>The data can be reused by researchers/stakeholders who want to compare our data with similar data collected in different countries, to perform textual analysis (content analysis and/or data mining) on our data. Teachers might also find it useful to explore the analysis as a basis for their practice. Also other stakeholders may be interested in reanalyzing our data for comparative aims.</p>
Data providers package for reporting monitoring results for veterinary medicinal product residues (official data reporting phase)
<p>This data providers package provides the data collection configuration and supporting materials for reporting veterinary medicinal product residues (VMPR) results according to Council Directive 96/23/EC of 29 April 1996 on measures to monitor certain substances and residues thereof in live animals and animal products and repealing Directives 85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/EEC. These are to be used for the official data reporting phase.</p> <p>The package includes:</p> <p>Advice on the values to be reported for the mandatory fields specified for this data collection.</p> <p>The Standard Sample Description Version 2 XML schema definition for VMPR reporting.</p> <p>The STX transformation file which automatically assigns sampEventId and sampAnId when this information is not provided.</p> <p>The general and VMPR specific business rules applied for the automatic validation of the submitted datasets.</p> <p>The VMPR specific terminologies to be used for reporting analytical methods and the residues included in the scope of the analytical methods.</p> <p>An example of a reportable dataset following the Quick Start Reporting guide.</p> <p>Excel Mapping tool to convert excel files after mapping into XML document.</p> <p>Guidance on how to use the Excel Mapping tool.</p> <p>Guidance on how to run the validation report after submitting data to the DCF.</p>
Final Report on Data Management - Raw data of DC-TRNG for D2.4 statistical testing
<p>Collected Raw & Post-processed data from DC-TRNG for both AIS-31 and NIST800-90B tests suites for Final Report on Data Management</p> <p>The purpose of the final report on data management is to provide an update of the analysis of the main elements of the data management policy used by the applications with regards to all the datasets that were generated by the project. Most important aspects regarding data management, like metadata generation, data preservation, and responsibilities, were updated compared to the initial report D5.2 (Data Management Plan) according to the outcome of the project.</p>
Scalasca analysis report for SPEC MPI.2007 benchmark 132.zeump2 on 512 processes in virtual-node mode on Blue Gene/P
<p>A Cube3 performance analysis report written by the Scalasca 1.x parallel analyzer of a measurement of the SPEC MPI 2007 benchmark 132.zeusmp2 (ZeusMP/2) executed in virtual-node mode on 512 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH.</p>
Scalasca analysis report of the ASCI Sweep3D benchmark on 294,912 processes in virtual-node mode on IBM Blue Gene/P with manually annotated iterations
<p>A Cube3 performance analysis report written by the Scalasca parallel analyzer of a measurement of the ASCI benchmark Sweep3D, executed in virtual-node mode on 294,912 processes of the IBM Blue Gene/P system JUQUEEN, operated by Forschungszentrum Jülich GmbH. The measurement includes system topology information and manual annotations of the twelve iterations.</p>
bin3C - Cluster report and CheckM result for bin3C solution of the real human gut microbiome
<p>Supplementary data table S3 from the manuscript</p> <p>bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</p> <p>A real human gut microbiome was deconvoluted using bin3C. Subsequently, bin3C produced a report detailing per-cluster statistics for the entire solution. The largest 296 clusters were then analyzed with CheckM and joined to the report.</p>
Tables and Figures complementing the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2016
<p>All tables, figures and maps produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2016 are provided.</p>
Annex B to the technical report on the raw primary commodity (RPC) model - Summary statistics of the output data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex B to the technical report on the raw primary commodity model:</strong></p> <p>Annex B is an excel file which presents summary statistics of the output data generated by the RPC model. The following tables are included in Annex B:</p> <p>Table B.1 :Summary statistics of chronic RPC consumption expressed in g/kg bw per day (total population)</p> <p>Table B.2 :Summary statistics of chronic RPC consumption expressed in g/day (total population)</p> <p>Table B.3 :Summary statistics of acute RPC consumption expressed in g/kg bw (consumers only)</p> <p>Table B.4 :Summary statistics of acute RPC consumption expressed in g (consumers only)</p> <p>Table B.5 :Comparison of the RPC consumption data with RPC consumption data used in EFSA's Pesticides Residues Intake Model (PRIMo)</p> <p>Table B.6 :Contribution of processed products to the average chronic RPC consumption</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.