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8,924 results for “formation”
Ice timing (formation or ice-on and clearance or ice-off) for Yellowstone Lake, Wyoming, USA (1927-2022)
Lakes are sentinels of environmental change. In cold climates, lake ice phenology–the timing and duration of ice cover during winter–is a key control on ecosystem function. Ice phenology appears to be driven by a complex interplay between physical characteristics and climatic conditions. Under climate change, lakes are generally freezing later, melting out earlier, and experiencing a shorter duration of ice cover; however, few long-term records exist for large, high-elevation lakes which may be particularly vulnerable to climate impacts. Here, we provide an ice phenology data over the last century (1927-2022) for North America’s largest high-elevation lake—Yellowstone Lake.
Field Evidence of Carbon and Nitrogen Stabilization through Mineral Associated Organic Matter Formation in Coastal Wetland Soils from Apalachicola, Florida, collected in June, 2022.
This data set was used to observe the role of Mineral Associated Organic Matter Formation (MAOM) on biogeochemical soil properties in three coastal wetlands in Apalachicola, Florida. One wetland was restored using beneficial dredged sediment, increasing the soil's inorganic matter content. Soil samples were collected in June 2022 from this wetland and two nearby reference wetlands: one with high organic matter and the other with higher inorganic matter content. The samples were analyzed at the University of Central Florida for biogeochemical properties to determine which properties were most related to MAOM pools.
ClimHyrdoDB Archive: Meteorologic and hydrologic observations from LTER and USFS sites, 2001-2020 - orignal database format
This dataset is an archive of the ClimHydroDB database, which was actively used from early 2001 to mid 2020. The database contained contributions from 62 contributors (primarily from the LTER Network and US Forest Service) and 672 research sites. Data records total approximately 16 million (raw) or 1.6 million (aggregated) for 22 meteorologic or hydrologic variables. This archive contains the 23 core tables of the ClimHydroDB database as text tables of comma separated values, plus the database entity relationship diagram (ERD), User Guide, database table descriptions (DDL, SQL script), and a zip file of related documents and presentations. Database design: At last upgrade, the database was implemented in Microsoft SQL Server 2008 (see DDL for more information). Database tables are primarily in a key-value pair arrangement, with controlled input for many fields, and extensive cross referencing. This design allows many types of descriptors to be assigned, e.g., for the types of activities taking place at research stations, or for physical parameters to describe a research area itself. The EML metadata for tables holding controlled vocabularies are described using the string “List of …”. Cross reference tables are described in metadata as such, including the parent table names. Database history: To facilitate intersite research within the LTER network, site data managers developed a system to provide climatic summaries dynamically, called ClimDB. Later funding from the U. S. Forest Service allowed the original database to be expanded to include hydrologic variables, and the combined database was renamed ClimHydroDB in 2003. The database also harvested real-time streamflow data from USGS gauging stations, using code developed by the Georgia Coastal Ecosystem LTER. As of 2021, the ClimHydroDB content is available as data packages from individual contributing sites, each containing identically formatted text tables in the ODM 1.1 format, for integration with CUAHSI tools
Catalysis of Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by D. andersoni (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the chromogenic substrate Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by <em>Dermacentor andersoni</em>, for fixed concentration of enzyme (and modulator) and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and <em>PNAS 116 (28) 13873-13878</em> (<a href="https://doi.org/10.1073/pnas.1905177116">https://doi.org/10.1073/pnas.1905177116</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Tos-Gly-Pro-Arg-p-nitroanilide in micromolar (µM) concentration</p> <p>Line 2: Concentration of enzyme (0.15 nM)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Catalysis of Ac-DEVD-AMC by procaspase-3 (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the fluorogenic substrate Ac-DEVD-AMC to the fluorescent substrate AMC by recombinant procaspase-3 obtained in yeast cell extracts, for fixed concentration of enzyme and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Ac-DEVD-AMC in micromolar (µM) concentration</p> <p>Line 2: Concentration of protein in yeast extract (0.123 mg/mL)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Silva SSU taxonomic training data formatted for DADA2 (Silva version 138)
<p>These DADA2-formatted training fasta files were derived from the Silva Project's version 138 release. See https://www.arb-silva.de/documentation/release-138/ for database and citation information. The Silva 138 database is licensed under Creative Commons Attribution 4.0 (CC-BY 4.0); see file "SILVA_LICENSE.txt". The fasta files were generated and checked for consistency with version 132 using the R code in the R-markdown document "silva-v138.Rmd".</p> <p>Version 2 removes the dependence on preprocessed files from mothur, which results in a greater number of bacterial and archeal sequences. It also includes a new version of the assignTaxonomy training set that goes through the species level for use with longer amplicons obtained from long-read amplicon sequencing.</p> <p>If you use these files, please cite one or both of the Silva references below (or at the above link) and the DADA2 paper (reference below). I also recommend citing or linking to the Zenodo record for this specific version in your Methods or published source code to record the specific taxonomic database files used in your analysis.</p> <p><strong>NOTE:</strong><strong> </strong>These Version 2 files are intended for use in classifying prokaryotic 16S sequencing data and are not appropriate for classifying eukaryotic ASVs. The new method implemented within DADA2 for constructing these files only includes 100 eukaryotic sequences for use as an outgroup.</p> <p><strong>NOTE:</strong> These Version 2 files have a known problem in 10/883 families and 114/3838 genera. See https://github.com/mikemc/dada2-reference-databases/blob/main/silva-138/v2/bad-taxa.csv for a list of affected taxa and https://github.com/benjjneb/dada2/issues/1293 for more information.</p>
U2- and U12-type intron classifications for Physarum polycephalum in BED format
<p>A BED file containing intron information for <em>Physarum polycephalum</em> introns classified as U2- or U12-type by <a href="https://github.com/glarue/intronIC">intronIC</a>. This data is associated with the following manuscript: https://doi.org/10.1101/2020.10.12.336362; the genome and annotation file used to identify the introns are available here: https://doi.org/10.5281/zenodo.4086119.</p> <p> </p> <p>The file columns are:</p> <p>1. Genome FASTA record name (scaffold)</p> <p>2. Intron start coordinate (0-indexed)</p> <p>3. Intron end coordinate (1-indexed)</p> <p>4. Intron label from intronIC</p> <p>5. U12-type probability score (0-100); introns with scores > 95 were considered U12-type in the manuscript</p> <p>6. Strand</p>
IPBES Data Management Tutorials - Session 3.4: Data management report details: File formats
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: File formats</em>, focuses on specific recommended file formats for text, tabular data, images, sound, and geospatial data. </p>
Formatting hemiclone Drosophila melanogaster genotype data for GWAS
<p>Data and code for generating filtering and formatting of Drosophila melanogaster genotype data, from the Sussex LHM hemiclone population sample.</p>
X-ray diffraction laboratory investigation for the Eptachori, Pentalofos and Tsotyli formations in West Macedonia
<p>The data comprises work under the Project Pilot Strategy GA No. 101022664, funded by the European Union. </p> <p>The work relates to rock samples collected in 2022 in West Macedonia, Greece. For full details, please refer to the following:</p> <ol> <li>Tsotyli formation: <a href="https://app.geosamples.org/sample/igsn/IE5770001">https://app.geosamples.org/sample/igsn/IE5770001</a> - <strong>WGS84 Lat : 40.3075, </strong><strong>WGS84 Long : 21.3354</strong></li> <li>Pentalofos formation: <a href="https://app.geosamples.org/sample/igsn/IE5770002">https://app.geosamples.org/sample/igsn/IE5770002</a> - <strong>WGS84 Lat : 40.1332,</strong> <strong>WGS84 Long : 21.1997</strong></li> <li>Eptachori formation: <a href="https://app.geosamples.org/sample/igsn/IE5770003">https://app.geosamples.org/sample/igsn/IE5770003</a> - <strong>WGS84 Lat : 40.1332, </strong><strong>WGS84 Long : 21.1997</strong></li> </ol> <p>The focus of the work is related to CO2 storage in appropriate saline aquifers in West Macedonia. Here are listed the raw results from the X-ray diffraction laboratory investigation.</p> <p>XRD equipment: Bruker D8 Advance</p> <p>Configuration parameters for XRD analysis: </p> <table> <tbody> <tr> <td>Type</td> <td>Locked couple</td> </tr> <tr> <td>Start</td> <td>3.000 degrees</td> </tr> <tr> <td>End</td> <td>93.009 degrees</td> </tr> <tr> <td>Step</td> <td>0.019 degrees</td> </tr> <tr> <td>Step time</td> <td>96s </td> </tr> <tr> <td>Temp</td> <td>25 centigrade (room) </td> </tr> <tr> <td>Time started</td> <td>0s</td> </tr> <tr> <td>2-Theta</td> <td>3.000 degrees</td> </tr> <tr> <td>Theta</td> <td>1.500 degrees</td> </tr> </tbody> </table>
Dataset for a publication: "A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation"
<p>These data are published as part of the paper: A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation. The structure and organization of the data are outlined in the readme file. </p> <p> </p>
cldf-datasets/szetosinitic: Chinese Structure Dataset from Szeto et al.'s (2018) paper in CLDF-Format
<p>This is a structural dataset originally published along with a paper by Szeto et al. (2018) on Chinese dialect classification:</p> <blockquote> <p>Szeto, P. Y.; Ansaldo, U. & Matthews, S.Typological variation across Mandarin dialects: An areal perspective with a quantitative approach Linguistic Typology, 2018, 22, 233-275.</p> </blockquote>
Evolution of cosmic star formation in the SCUBA-2 Cosmology Legacy Survey
<p>This dataset consists of tabulated data from the figures included in the referenced publication. The following datasets are included:</p> <p>Stacked SFR obscuration (IRX=IR/UV) of UVJ-selected star-forming galaxies:</p> <ul> <li>Weighted mean IRX as a function of Muv & stellar mass (Figure 12): MUV_irx1.dat</li> <li>Weighted mean IRX as a function of beta, over all masses and redshifts: beta_irx.dat</li> <li>Weighted mean IRX as a function of beta, binned by stellar mass (Figure 13): beta_irx_mstar.dat</li> <li>Weighted mean IRX as a function of beta, binned by redshift (Figure 14): beta_irx_z.dat</li> </ul> <p>Cosmic SFR density as a function of redshift for massive galaxies log(Ms/Msol)>10 (Figure 15):</p> <ul> <li>All mass-selected galaxies: sfrd_massive.dat</li> <li>UV-luminous galaxies Muv<M*; log(Ms/Msol)>10: sfrd_hiLUV.dat</li> <li>IR-luminous galaxies detected at 450µm: sfrd_IRdet.dat</li> </ul> <p> </p> <p>Cosmic SFR density as a function of redshift corrected to all stellar masses (Figure 16):</p> <ul> <li>All mass-selected galaxies: sfrd_uvlfcorr.dat</li> <li>UV-luminous galaxies Muv<M*; log(Ms/Msol)>10: sfrd_hiLUV_uvlfcorr.dat</li> </ul> <p>Full details of the binning and stacking methodology are explained in the paper.</p>
Towards a Deeper Understanding of Impression Formation – New Insights Gained From a Cognitive-Ecological Perspective
<p>data from:</p> <p>Prager, J., Krueger, J. I., & Fiedler, K. (2018). Towards a deeper understanding of impression formation—New insights gained from a cognitive-ecological perspective. <em>Journal of Personality and Social Psychology</em>, <em>115</em>(3), 379–397. https://doi.org/10.1037/pspa0000123</p>
Genomes plasmids MDR B. fragilis ONT sequence read files in fastq format
<p>Supporting data for the manuscript <em>Complete genome assembly of clinical multidrug resistant Bacteroides fragilis isolates enables comprehensive identification of antimicrobial resistance genes and plasmids.</em></p> <p>Oxford Nanopore reads demultiplexed with <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwjNjqL5tY7iAhUawMQBHZHfDasQFjAAegQIAhAB&url=https%3A%2F%2Fgithub.com%2Frrwick%2FDeepbinner&usg=AOvVaw0wikvIUagLuFV38CwKZtia">Deepbinner</a> v0.2.0 and base-called (with demultiplexing) using Albacore v2.3.3. Barcodes and adapters were removed with <a href="https://github.com/rrwick/Porechop">Porechop</a> v0.2.4 with the --discard_middle option.</p> <p>Data from each isolate was produced from two runs per isolate. Data for the individual runs are included here. They can easily be concatenated eg with cat. Runs are named TVS_01,. TVS_02, TVS_03 and TVS_04.</p> <p>Fast5 (only demultiplexed with deepbinner and basecalled with albacore) as well as illumina reads and genome assemblies can be found via the NCBI bioproject accessions:</p> <p>Isolates, NCBI bioproject accession no:</p> <p>CCUG4856T, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA525024">PRJNA525024</a></p> <p>BFO17, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244943">PRJNA244943</a></p> <p>BFO18, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244944">PRJNA244944</a></p> <p>S01, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244942">PRJNA244942</a></p> <p>BFO42, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA253771">PRJNA253771</a></p> <p>BFO67, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254401">PRJNA254401</a></p> <p>BFO85, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254455">PRJNA254455</a></p> <p> </p> <p><strong>md5sum's (also found in the file md5.md5):</strong></p> <p>135d0570a1e49e25c8fde59f321cca68 BFO17_TVS_03_99377.barcode02_trimmed.fastq.gz<br> 3c6ca800a1f735937c0cffccd263fb82 BFO18_TVS_01_97673.barcode03_trimmed.fastq.gz<br> dc37823950f529d3a859b7a7c514af8c BFO18_TVS_03_99377.barcode03_trimmed.fastq.gz<br> abd707404f9ebbc38652e45ed378ca21 BFO42_TVS_02.barcode10_trimmed.fastq.gz<br> 9761e9ab082e276624c5778dcbeaffd2 BFO42_TVS_04.barcode10_trimmed.fastq.gz<br> ffc27009c0f7fead1af84ce045dbe5f3 BFO67_TVS_02.barcode09_trimmed.fastq.gz<br> 06c363a7feeeb88f9d195b3769b37f2b BFO67_TVS_04.barcode09_trimmed.fastq.gz<br> 5553c95cc98f4b9d4cfb38c4f8f8f037 BFO85_TVS_02.barcode08_trimmed.fastq.gz<br> b1a8013cba7a079cee6d3bdd6cd97ff2 BFO85_TVS_04.barcode08_trimmed.fastq.gz<br> 8169225219a5fb20935d5f0304aa80c5 CCUG4856T_TVS_01_97673.barcode01_trimmed.fastq.gz<br> a509b0ae912a798a91e677795198c1c6 CCUG5846T_TVS_03_99377.barcode01_trimmed.fastq.gz<br> 8a9d2eb8b626a6e87ed267d31aa220e3 S01_TVS_01_97673.barcode04_trimmed.fastq.gz<br> 69e239a17becc25a4f103dfc3bf5886a S01_TVS_03_99377.barcode04_trimmed.fastq.gz</p>
Dataset to "Persistent Identifiers for File Formats: enabling preservation and re-use of research data"
<p>This fileset includes a "preprint" and the main dataset <em>fileformatRecognizer</em> (as .xlsx and .csv) to the paper "Persistent identifiers for file formats: enabling preservation and re-use of research data" submitted to iPRES 2019, but subsequently rejected after peer review. For the sake of transparency, permission to make available here the anonymous reviews motivating the rejection (<em>ReviewsPIDs4fileFormats.odt</em>) was asked, but was left without response. Some images (screendumps) and text result files from file identification tools tested are included. Further, a simple xquery command file (BaseX) for <em>fetch:content-type</em>()<em>, </em>used for getting MIME-types for files, is also provided.</p>
Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'
<p>All numerical data used in the manuscript <strong>“Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” </strong>by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided.</p> <p>These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr)</p> <p>Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340 (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> )</p> <p>For an in-depth understanding of the description below, a study of the above mentioned manuscript is required.</p> <p>(A) Station model results</p> <p>The station results can be found in files with filenames of the form:</p> <p>station $MODEL.nc</p> <p>The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file.</p> <p>In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as:</p> <p>st $STATION $FIELDhour st $STATION $FIELD month</p> <p>where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable</p> <p>st Finokalia CCN02 hour</p> <p>found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN<sub>0<em>.</em>2 </sub>at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model.</p> <p>st Vavihill DU month</p> <p>Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours)</p> <p>(B) Station observational results</p> <p>There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is</p> <p>station observations.nc</p> <p>while the following fields are contained in there:</p> <p>st $STATION $FIELDhour</p> <p>st $STATION $FIELD month</p> <p>The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch).</p> <p>(C) Station Multi-model Median</p> <p>Monthly averages of the models can be found in the file</p> <p>station MMM.nc</p> <p>The following fields can be found in the file</p> <p>st $STATION $FIELD month median</p> <p>st $STATION$FIELD month quart25</p> <p>st $STATION$FIELD month quart75</p> <p>where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively.</p> <p>(D) Global model results</p> <p>In the following single file can be found for each of the models the surface distribution of various fields.</p> <p>results global models year2011.nc</p> <p>They correspond to the annual mean of the year 2011. The resolution of the grid is 1<sup>◦ </sup>× 1<sup>◦</sup>. The file contains the following variables:</p> <p>$FIELD $MODEL</p> <p>The $FIELD and $MODEL can be found in Tables 3 and 1, respectively.</p> <p>(E) Global average results</p> <p>In the file</p> <p>surface_ global_average_year2011.nc</p> <p>can be found in 5<sup>◦</sup>×5<sup>◦ </sup>resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as:</p> <p>med $FIELD</p> <p>div $FIELD</p> <p>where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results.</p> <p>Tables and details on the fields provided are given in the readme file.</p>
Laboratory simulations of benzene oxidation and formation of highly oxygenated organic molecules (HOM)
<p>This dataset supplements the following manuscript:<br> Garmash, O., Rissanen, M. P., Pullinen, I., Schmitt, S., Kausiala, O., Tillmann, R., Percival, C., Bannan, T. J., Priestley, M., Hallquist, Å. M., Kleist, E., Kiendler-Scharr, A., Hallquist, M., Berndt, T., McFiggans, G., Wildt, J., Mentel, T., and Ehn, M.: Multi-generation OH oxidation as a source for highly oxygenated organic molecules from aromatics, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-582, in review, 2019.<br> It presents data from Table 1, Tables S1-S4 and Figures 5, A1 and A2, including model input data.</p>
DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea
<p><strong><em>This version is to stay up to date with the improvements and increase in 16S rRNA gene sequences (SSU) added to the GTDB release 220. Please read this post for the stats on the updates. </em></strong><strong><em>https://gtdb.ecogenomic.org/stats/r220 </em></strong><strong><em>.</em></strong><strong><em> </em></strong></p> <p><strong><em>There has been no change to the RDP-RefSeq reference database please use previous versions.</em></strong></p> <p><strong><em>If anyone has concerns with MAG extracted 16S rRNA gene contamination concerns, then I suggest that they contact the curators of GTDB themselves because it is outside of my role with these resources designed for DADA2 usage only. </em></strong></p> <p><strong><em>Another concern that was raised was the orientation of the DB sequences, to get past this problem please use the tryRC = TRUE argument in the assignTaxonomy command within DADA2, this will search your ASVs in the reverse complement as well. </em></strong></p> <p>The bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted to use the "assignTaxonomy" command within the DADA2 pipeline. The data was converted to suite DADA2 format by Alishum Ali.</p> <ol> <li>Genome Taxonomy Database (GTDB): The new version of our dada2 formatted GTDB reference sequences now contains 58102 bacteria and 3672 archaea full 16S rRNA gene sequences. If you wonder why there are fewer species with 16S rRNA, that is because some metagenomics-assembled genomes (MAGs) lack the 16S gene and thus cannot be extracted. The database was downloaded from <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/">https://data.ace.uq.edu.au/public/gtdb/data/releases/</a> on 24/10/2024. Please read the release notes and file descriptions. </li> </ol> <p>The formatting to DADA2 was done using simple awk bash scripts. The script takes as input a fasta file and a tab-delimited taxonomy file (slightly edited to remove special characters) and then it outputs a fasta file with all 7 taxonomy ranks separated by ";" as required for DADA2 compatibility. Additionally, we have concatenated the unique sequence GTDB ID to the species entry (but replaced the "." with an " _". We see this as an important QC step to highlight the issues/confidence associated with short-read taxonomy assignment at the finer rank levels.</p> <p>Also, this update includes two other files that you can use with the assignTaxonomy and addSpecies commands in DADA2.</p>
Simulation systems for: "Pore formation in complex biological membranes: torn between evolutionary needs"
<p>Simulation systems for the publication:</p> <div> <div> <div> <p>Leonhard J. Starke, Christoph Allolio, and Jochen S. Hub, <em>Pore formation in complex biological membranes: torn between evolutionary needs</em>, BioRxiv (2024), doi: <a href="https://doi.org/10.1101/2024.05.06.592649">10.1101/2024.05.06.592649</a></p> <p>Required software:<br>GROMACS Chain Coordinate, a modified GROMACS variant for pore formation across membranes or stalk formation between membranes: <a href="https://gitlab.com/cbjh/gromacs-chain-coordinate">https://gitlab.com/cbjh/gromacs-chain-coordinate</a></p> <p>See README_small.sh and README_large.sh files for instructions on how to run pulling simulations for inducing pores in the provided complex membrane models.</p> </div> </div> </div>
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.