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6,467 results for “process”
Soil N pools and process rates for catenas at Caribou-Poker Research Watersheds and the Kuparuk River
Data describe soil NH4+ and NO3- pools, potential rates of denitrification and nitrification, and net N mineralization and nitrification. Soil pH, moisture, and organic matter are also included. Soils were sampled along a hillslope catena at hillslope, toeslope, and valley bottom locations and resolved to 5 cm depth increments. One hillslope at Carbiou-Poker Creeks Research Watersheds and one hillslope within the upper Kuparuk River basin were sampled. Samples were collected August 2009.
Assembled file of one minute averages for high resolution surface meteorological (Met) and sea water intake (SWI) data from continuous underway measurements from CCE LTER process cruises in the CCE region, 2006 - 2019.
As the research vessel is underway for the duration of a CCE Process Cruise (since 2006, ongoing), 30 parameters are continuously measured regarding the oceanographic surface and atmospheric and navigational environment of the vessel, along the ship's trackline in the CCE region.
Measurements, from CCE LTER process cruises in the California Current region, of total dissolvable inorganic concentrations of the nutrient iron, 2017 - (ongoing).
Seawater profiles or discrete samples for total dissolvable iron (TDFe) analysis are collected with the trace metal rosette or GO-FLO bottles, respectively, in stations located within the CCE LTER region in the California Current System. The total dissolvable iron concentration is obtained by collecting unfiltered seawater samples in 250 mL LDPE bottles and storing them at pH 1.8 for 6 months or more. The acidified seawater sample is then filtered using acid cleaned 0.4 um PES membrane Titan 3 syringe filters and determined by flow injection chemiluminescent (FI-CL) analysis at Scripps Insitution of Oceanography since 2017 (ongoing).
Conductivity Temperature Depth (CTD) Log of CTD casts from CCE LTER process cruises in the CCE region, 2006 - 2019 (ongoing).
Individual casts of Conductivity, Temperature and Depth (CTD) are logged on CCE Process cruises (since 2006, ongoing) in the Southern California region. The log includes time, location, number of bottles, cast and event numbers and other information about CTD casts.
Plant species percent cover data: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Soil nitrogen: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Soil carbon: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Local plant diversity and soybean biological control 2011 Harvest Measures:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Local plant diversity and soybean biological control 2012 Aphid Surveys:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Whole plot plant tissue chemistry: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Soil Cation Exchange Capacity by the summation method CEC, Ca, Mg, K: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Development of Symbolic Number Processing
Open the record for dataset details and reuse information.
LCI data for materials and processes comparing energy and water use of aqueous and gas-based metalworking fluids
<p>Datasets containing life cycle inventories for materials and processes, and results of the analysis in the article titled, "Comparing energy and water use of aqueous and gas-based metalworking fluids" published in the <em>Journal of Industrial Ecology.</em></p>
Post-processed output from WACCM: historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN) and sensitivity studies (compset FW)
<p>This dataset contains post-processed monthly output from WACCM (version 4) simulations:</p> <ol> <li>historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN, B55TRWCN.zip)</li> <li>sensitivity study: 50-years experiments designed with different geomagnetic and solar conditions (compset FW, FW.zip)</li> </ol> <p>The postprocessing involved (1) zonal averaging, (2) Transformed Eulerian mean (TEM) budget calculation in log-pressure coordindates from model output (h0 files).</p> <p><strong>Contact</strong><br> Monika Szelag (monika.szelag@fmi.fi)</p> <p><strong>Raw data</strong><br> The raw, ungridded monthly data (2TB, h0 files) are currently available on FMI storage system, under path /ibrix/arch/waccm, and can be requested through the contact person. </p> <p><strong>Dataset contents</strong></p> <pre><code>B55TRWCN.zip files: vdk.zonal.002.1957-2005.nc vdk.zonal.003.1957-2005.nc vdk.zonal.004.1957-2005.nc tem.vdk.zonal.002.1957-2005.nc tem.vdk.zonal.003.1957-2005.nc tem.vdk.zonal.004.1957-2005.nc</code></pre> <p>WACCM output: historical simulations, zonal averages, 3 ensemble members of 49 years each (002,003,004).</p> <p>Variables are <strong>NOY, O3, OH, QJOULE, QRL_TOT, QRLNLTE, QRS_TOT, T, U</strong> (vdk.zonal.00[234].1957-2005.nc) and <strong>delf, fphi, fz, vres, wres, z3</strong> (tem.vdk.zonal.00[234].1957-2005.nc</p> <pre><code>FW.zip files: zonal_F2000_max.nc zonal_F2000_avg.nc zonal_F2000_min.nc tem_zonal_F2000_max.nc tem_zonal_F2000_avg.nc tem_zonal_F2000_min.nc</code></pre> <p>WACCM output: perpetual year 2000, zonal averages, 50 years experiments with 3 different solar and geomagnetic conditions (min, max, avg).</p> <p>Variables are <strong>NOY, NOX, O3, OH, QRL_TOT, QRS_TOT, T, U, V, CO, CO2, PS, TS</strong> (zonal_F2000_*.nc) and <strong>delf, fphi, fz, vres, wres, z3</strong> (tem_zonal_F2000_*.nc)</p> <p> </p>
Pre-processed B cell receptor repertoire sequencing data from BioProject PRJNA527941
<p><strong>Data Processing</strong></p> <p> </p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p> </p> <p><strong>software_versions</strong> pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p><strong>quality_thresholds</strong> FilterSeq.py pRESTO Q>20</p> <p><strong>paired_reads_assembly</strong> AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong> MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p><strong>consensus_building</strong> BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong> CollapseSeq.py pRESTO</p> <p><strong>germline_database </strong>IMGT</p> <p> </p> <p><strong>Format</strong></p> <p> </p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>C_CALL </strong>Isotype subclass</p> <p><strong>SEQUENCE_ID </strong>Sequence identifier</p> <p><strong>V_CALL </strong>V segment gene and allele</p> <p><strong>D_CALL </strong>D segment gene and allele</p> <p><strong>J_CALL </strong>J segment gene and allele</p> <p><strong>JUNCTION_LENGTH </strong>Junction length</p> <p><strong>CONSCOUNT </strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT </strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE </strong>Constant region primer (isotype)</p> <p><strong>MU_COUNT_CDR_R </strong>Number of replacement mutations in CDR region</p> <p><strong>MU_COUNT_CDR_S </strong>Number of silent mutations in CDR region</p> <p><strong>MU_COUNT_FWR_R </strong>Number of replacement mutations in FWR region</p> <p><strong>MU_COUNT_FWR_S </strong>Number of silent mutations in FWR region</p> <p><strong>MUT_TOTAL </strong>Total number of mutations in V gene </p> <p><strong>SEQUENCE_INPUT </strong>Full length sequence</p> <p><strong>SEQUENCE_IMGT </strong>Gapped IMGT sequence</p> <p><strong>V_GERM_START_VDJ </strong>position of the first nucleotide in ungapped V germline sequence alignment</p> <p><strong>JUNCTION </strong>Junction nucleotide sequence</p> <p><strong>GERMLINE_IMGT_D_MASK </strong>IMGT-gapped germline nucleotide sequence with ns masking the NP1-D-NP2 regions</p> <p><strong>Run </strong>ID of sequencing run</p> <p><strong>Sample_type </strong>The tissue sampled (e.g Peripheral Blood, bone marrow, ..)</p> <p><strong>Sex </strong>Sex of the Subject</p> <p><strong>Age </strong>Age of the subject</p> <p><strong>UNIQUE_ID </strong>Subject identifier </p> <p><strong>SAMPLE_ID </strong>Sample identifier, linking back to raw data</p> <p><strong>Subset </strong>Defined B cell subset </p> <p><strong>Repertoire </strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR </strong>R/S ratio in CDR region</p> <p><strong>R_SFWR </strong>R/S ratio in FWR region</p> <p><strong>V_FAM </strong>V family gene</p> <p><strong>V_GENE </strong>V segment gene</p> <p><strong>D_GENE </strong>D segment gene</p> <p><strong>J_GENE </strong>J segment gene</p> <p><strong>Clust_Rank </strong>Cluster rank</p> <p><strong>Clust_REPRES </strong>Cluster representative</p> <p><strong>Clust_SIZE </strong>Cluster size</p> <p><strong>Clust_MAXFREQ </strong>Cluster maximum frequency</p> <p><strong>Clust_SHAREDNESS </strong>Cluster sharedness</p> <p><strong>CDR3_AA_GRAVY </strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_CHARGE </strong>CDR3 charge</p> <p><strong>CDRH3PDB </strong>CDRH3 PDB (Structure) code</p> <p><strong>H1Canon </strong>H1 Canonical class</p> <p><strong>H2Canon </strong>H2 Canonical class</p> <p><strong>H1_GERMLINE </strong>H1 Germline Canonical class</p> <p><strong>H2_GERMLINE </strong>H2 Germline Canonical class</p> <p> </p> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. <em>Genome Res.</em>21: 936–939.</p>
Process Mining from Information-SeekingConversations
<p>This deposit contains the results from the paper "Process Mining from Information-Seeking Conversations" submitted to WCCI 2020. The results are in .xes format, and ready to be analyzed in a process mining tool. </p>
Head-Neck-Radiomics-HN1 data processed with Pyradiomics ready to use for data analysis
<p>We downloaded <a href="https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-Radiomics-HN1">Head-Neck-Radiomics-HN1, </a>align CT and segmentation images and use PyRadiomics to extract all kind of features for data analysis.</p> <p>CSV contains all the features. Some of them are settings information, setting information should be remove before any data analysis. By using feature extraction algorithm or... by checking values, all the settings have the same values in the columns. Each row is a patient.</p> <p> </p> <p> </p>
Processed Saccharomyces cerevisiae transcriptomics and genomics data for machine learning
<p><strong>Genomic data including open reading frame (ORF) boundaries of Saccharomyces cerevisiae C288 was obtained from the Saccharomyces Genome Database (<a href="https://www.yeastgenome.org/">https://www.yeastgenome.org/</a>) (<a href="http://paperpile.com/b/QmuOBv/gg2yy">Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–5 (2012)</a>) and published data (<a href="http://paperpile.com/b/QmuOBv/g4EbQ">Xu, Z. et al. Bidirectional promoters generate pervasive transcription in yeast. Nature 457, 1033–1037 (2009)</a>, <a href="http://paperpile.com/b/QmuOBv/uJqAw">Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008)</a>). </strong><strong>Coding regions were extracted based on ORF boundaries and codon frequencies were normalized to probabilities. Processed raw RNA sequencing Star counts were obtained from the Digital Expression Explorer V2 database (<a href="http://dee2.io/index.html">http://dee2.io/index.html</a>) (<a href="http://paperpile.com/b/QmuOBv/LCGD">Ziemann, M., Kaspi, A. & El-Osta, A. Digital expression explorer 2: a repository of uniformly processed RNA sequencing data. GigaScience vol. 8 (2019)</a>) and filtered for experiments that passed quality control. Raw mRNA data were transformed to transcripts per million (TPM) counts and genes with zero mRNA output (TPM < 5) were removed. Prior to modeling, the mRNA counts were Box-Cox transformed.</strong></p>
"MiRoR13-P1- A scoping review on the roles and tasks of peer reviewers in the manuscript review process in biomedical journals"
<p>This dataset is related to the publication "A scoping review on the roles and tasks of peer reviewers in the manuscript review process in biomedical journals". All additional data can be found under 'Additional files' in the publication</p>
Enhanced reforming of mixed biomass tar model compounds using a hybrid gliding arc plasma catalytic process
<p>Datasheet for the paper; DOI: 10.1016/j.cattod.2019.05.046</p>
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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.