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569 results for “APRIL”
Data from: "Characterization of two Iberian freshwater fish transcriptomes, Squalius carolitertii and Squalius torgalensis, living in distinct environmental conditions" in Genomic Resources Notes Accepted 1 April 2015 to 31 May 2015
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Top 100-Ranked Clinical Journals' Preprint Policies as of April 23, 2020
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Weekly United States COVID-19 Racial Data By State, April 12, 2020 to March 7, 2021
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Chlorophyll and phaeopigments measured from discrete bottle samples from CCE LTER P0904 student cruise in the California Current System, determined by extraction and bench fluorometry, April 2009.
Discrete bottle samples taken from various depths in the California Current System aboard CCE LTER P0904 student cruise. Samples were frozen aboard the ship and taken back to the lab for extraction and analysis. Samples are filtered (known volumes) onto GF/F filters. The filters are placed into culture tubes containing 90% acetone, and the fluorescence of the samples is read on a fluorometer after 24 to 48 hours. The samples are then acidified to degrade the chlorophyll to phaeopigments (non-photosynthetic pigments) and a second reading is taken. The readings prior to and after acidification are used to calculate concentrations of both chlorophyll a and phaeopigments (i.e. phaeophytin).
Parameters from discrete bottle samples on a hydrographic CTD (Conductivity Temperature Depth) cast during CCE LTER P0904 student cruise, April 2009.
Hydrographic CTD (Conductivity Temperature Depth) measurements from discrete water samples are taken on individual CTD rosette casts, deployed on CCE P0904 student cruise in the Southern California region. Seawater is collected in up to 24 Niskin bottles at specific depths (up to 1000m), determined by the chlorophyll maximum and mixed layer depth as the instrument is lowered through the water column. The samples are filtered and analyzed at sea and used to assess the CTD sensor data quality. Measurements include depth, temperature, salinity, density (sigma theta), oxygen, O2 saturation, PAR (radiation, surface radiation and irradiance), fluorescence and transmission. Salinity checks are performed with a salinometer back at the lab.
Conductivity Temperature Depth (CTD) sensor profile data binned by depth from stations within the CCE region from CCE LTER P0904 student cruise, April 2009.
Data from deployed Seabird 911 CTD mounted on a 24-bottle rosette during P0904 student cruise in the CCE region. The CTD-rosette is lowered into the ocean (to depths up to 1000m) at selected hydrographic stations and multiple times across fronts, using the ship's conductive-wire winch. Data from many sensors are transmitted up the conductive wire and displayed real-time on a data aquistion computer. Discrete seawater samples are collected in 10L bottles at specific depths determined by the chlorophyll maximum and mixed layer depth. These samples are analyzed at sea and used to assess the CTD sensor data quality, plus measure additional properties. Processed CTD profile data are binned by depth and include: depth, temperature, salinity, density (sigma theta), oxygen, O2 saturation, PAR (radiation, surface radiation, and % irradiance), fluorescence, transmission, and nitrate. These data are then compared to the seawater sample data, and corrected if necessary.
Dissolved organic carbon (DOC) taken from discrete water column samples collected between October and April at Palmer Station, 2002-2012.
Dissolved organic carbon (DOC) is a poorly-characterized but large and dynamic pool of actively-cycling carbon in the oceans, and one of the largest organic carbon pools on the planet. The total DOC pool consists of three major fractions: refractory DOC resistant to microbial oxidation with a turnover time of millennia; semi-labile DOC, produced and decomposed on seasonal timescales, and labile DOC, consisting of simple, recently-produced compounds with nanomolar concentrations, and turnover times of minutes-days. The background concentration of refractory DOC in the deep ocean is 35-45 micromolar. DOC concentration in the upper 100-200 meters is enhanced by 10-50 micromolar with the addition of semilabile DOC. In subtropical and temperate oceans, semilabile DOC can form an important part of the carbon export by deep vertical mixing into the oceanic mid-depths. Concentrations of semilabile DOC are lower in the polar Southern Ocean than in most other regions.
TweetsKB (Part 8, May 2019 - April 2020)
<p><strong>TweetsKB</strong><strong> </strong>is a public RDF corpus of anonymized data for a large collection of <strong>annotated </strong>tweets. The dataset currently contains data for more than <strong>1.9 billion tweets</strong>, spanning more than 7 years (February 2013 - April 2020). Metadata information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong> and <strong>URLs </strong>are exposed in RDF using established RDF/S vocabularies*. Example queries and more information are available through TweetsKB's home page: <a href="https://data.gesis.org/tweetskb/">https://data.gesis.org/tweetskb/</a>.</p> <p> </p> <p><em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em></p>
Three-dimensional numerical simulation of the interseismic and coseismic phases associated with the 6 April 2009, Mw 6.3 L'Aquila earthquake (Central Italy)
<p>Results of the numerical model expressed in terms of nodal stresses, strains and displacements at the end of the interseismic and coseismic phases.</p>
GInSAR vertical surface displacements over the San Joaquin Valley, CA spanning April 2015 to October 2017
<p>A time series of vertical surface displacements over the San Joaquin Valley (SJV) spanning April 2015 to October 2017.</p> <p>Base Data: European Space Agency’s Sentinel-1A/B descending track 144; National Science Foundation Plate Boundary Observatory Global Positioning System (GPS) network; Southern California Earthquake Center’s Community Geodetic Model</p> <p>Time: 2015.04.01 – 2017.10.23, 51 acquisition times, 263 interferograms</p> <p>Processors/Software: GMTSAR, MATLAB</p> <p>Interferogram construction and unwrapping was performed in GMTSAR. GPS correction, time series inversion, and vertical projection was performed in MATLAB.</p> <p> </p> <p>This dataset was constructed following similar methods outlined in Neely et al., (2020).</p> <p>Neely, Wesley R., Adrian A. Borsa, and Francesca Silverii. “GInSAR: A CGPS Correction for Enhanced InSAR Time Series.” <em>IEEE Transactions on Geoscience and Remote Sensing</em> 58, no. 1 (January 2020): 136–46. <a href="https://doi.org/10.1109/TGRS.2019.2934118">https://doi.org/10.1109/TGRS.2019.2934118</a>.</p> <p> </p> <p>This dataset is provided as a “.mat” compatible with MATLAB. Contents include:</p> <p>TS – 3110x2940x51 matrix of the vertical surface displacements (mm) relative to April 1, 2015</p> <p>X – 3110x2940 matrix of the pixel longitudes (decimal degrees)</p> <p>Y – 3110x2940 matrix of the pixel latitudes (decimal degrees)</p> <p>rate – 3110x2940 matrix of the estimated vertical velocity (mm/yr) over April 2015 to October 2017</p> <p>time – 51x1 array of the dates for each acquisition in MATLAB data. Provided as integer day where time(1) = 736055 à April 1, 2015</p> <p> </p> <p>Example plotting in MATLAB:</p> <p>% plot the rate map</p> <p>figure</p> <p>pcolor(X,Y,rate)</p> <p>shading flat</p> <p> </p> <p>% plot the 25<sup>th</sup> time slice map of the displacement time series</p> <p>figure</p> <p>pcolor(X,Y,squeeze(TS(:,:,25))</p> <p>shading flat</p> <p> </p> <p>% plot the time series at location X(1500,1500) and Y(1500,1500)</p> <p>figure</p> <p>plot(time,squeeze(TS(1500,1500,:)))</p>
Data from: "Transcriptomic resources for the critically endangered stellate sturgeon Acipenser stellatus" in Genomic Resources Notes accepted 1 April 2015 – 31 May 2015
This article documents the public availability of transcriptomic resources of the non-model and critically endangered starlet sturgeon Acipenser stellatus.
Data quality definitions systematic search; data and coding (2008-2024 april, version edit Dimensional-definitions worksheet)
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FIGURE 9. Thallasobathia pelagica, ZMUC P77853. Denmark Strait, 18 April 1991 in A checklist of the fish fauna of Greenland waters 2378
FIGURE 9. Thallasobathia pelagica, ZMUC P77853. Denmark Strait, 18 April 1991. Photo PRM.
Data from: "Transcriptome sequences for Campanula gentilis" in Genomic Resources Notes accepted 1 April 2015 – 31 May 2015
In this report, we present the transcriptome of a single accession of Campanula gentilis Kovanda, obtained through the sequencing of both a normalized and a non-normalized cDNA library generated from stem and leaf tissue. The resources we provide include the raw sequence reads, the assembled contigs, the putative open reading frames, the contig/ORF annotations and the normalized as well as non-normalized expression levels.
Data from: "RAD Sequencing for SNP Discovery in Two Populations of Bighorn Sheep (Ovis canadensis)" in Genomic Resources Notes accepted 1 April 2013 - 31 May 2013
In this work we present the development of a large set of single nucleotide polymorphisms (SNPs) discovered in two populations of bighorn sheep (Ovis canadensis). To do so we used restriction-site associated DNA (RAD) sequencing of four individuals from each population. Through alignment of reads to the domestic sheep (Ovis aries) genome we discovered >83,000 SNPs, of which >38,000 are suitable for assays such as an Illumina SNP chip. These loci will allow for fine-mapping of loci associated with horn size, and examination of the consequences of the genetic rescue including mapping genes underling differences in life-history characteristics.
Microseismic event catalogue for the April 2022 third stimulation at FORGE Enhanced Geothermal System (Utah, US) generated applying a semblance-based detection method to DAS data
<p>The dataset contains microseismic event detections for the third stimulation stage (started on 21 April 2022 at 13:48:22 UTC) performed at FORGE Enhanced Geothermal System (Utah, US) (EGIUU, 2022). We created the catalogue of detections using the semblance-based detection method described in Porras et al. (2024). We applied the detector only to the optical fibre in the observational well 78B-32 (EGIUU, 2022). </p> <p>The catalogue contains events detected from 21 April 2022 at 13:00 UTC to 22 April 2022 at 13:00 UTC and is compared with the Short-Term Average/Long-Term Average catalogue created by Silixa LLC for the same time interval (EGIUU, 2022).</p> <p>The semblance-based detection method detects 2187 events in comparison to the 1169 events reported in the Silixa LLC catalogue. Notice that both catalogues are declustered by removing detections less than 0.7 seconds apart (we keep the earliest detection in case of two or more events within 0.7 seconds). We inspect for common events in both catalogues by considering a time interval of ± 0.6 seconds from the detection time of the events in our catalogue (in the case of more than one event we keep only the closest detection). We find 1086 common events, 83 events only contained in the Silixa's catalogue, and 1101 events only contained in the catalogue from Porras et al. (2024). We manually inspect all the detected events and classify as false detection 6 out of the 1086 events common to both catalogues, 64 out of the 83 events only in the Silixa's catalogue, and 116 of the 1101 events only in the catalogue from Porras et al. (2024). False detections, in addition to noise where no clear P and/or S-waves are visible, include codas of microseismic events (e.g. the same event is picked multiple times especially in the cases where the events occur across two files), and seismic events not related to the FORGE stimulation. In summary, our catalogue contains 2187 microseismic events with 122 potential false detections (5.6 %), while the Silixa's catalogue contains 1169 microseismic events with 70 potential false detections (6.0 %).</p> <p>In this data repository we also include a catalogue "microseismicity_catalogue.csv" with the detection times, the catalogue to which the detection belong (i.e., common detection to both Silixa's catalogue and the catalog from this study, only the Silixa's catalogue, only the catalogue from this study), and a description of the detection (i.e., real event, or false detection). We also upload the figures of all the detections where the figure name correspond to the detection time. The figures are obtained using the original sampling rate of the data (i.e., 4 kHz) and applying a Frequency-Wavenumber (FK) filter to remove the K=0 and unwanted energy (an example of the used F-K filter is also included in this repository). Traces in the figures have amplitudes normalized between -1 and 1. </p> <p>For additional details about the method used to obtain the catalogue we refer the reader to Porras et al. (2024)</p> <p>References:</p> <p>EGIUU, Energy and Geoscience Institute at the University of Utah. (2022). Utah FORGE: Well 16A(78)-32 2022 Stimulation Silixa Microseismic Report [data set]. Retrieved from https://dx.doi.org/10.15121/1893532.</p> <p>Porras, J., Pecci, D., Bocchini, G. M., Gaviano, S., De Solda, M., Tuinstra, K., Tognarelli, A., Lanza, F., Stucchi, E., and Grigoli, F. (2023). A Semblance-based Microseismic Event Detector for DAS Data. Geophysical Journal International, ggae016, https://doi.org/10.1093/gji/ggae016</p>
Supplementary material 4 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
The interactive Krona chart associated with Figure 2 :
Supplementary material 2 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
The MIxS-BE annotations implemented for the built environment sequences during the workshop :
Figure 3 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 3 Analysis of the MIxS-BE "building occupancy type" (type of building where the underlying sample was taken). The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
Figure 2 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 2 Krona chart of the taxonomic affiliation of the built environment sequences down to order level. The Krona chart lists all annotated built environment sequences except those classified as Fungi sp. (32%) and those of non-fungal origin (1%). An interactive version of the Krona chart is provided as Supplementary material 4. The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
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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.