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7,195 results for “SET”
Sediment Elevation Change (SET Method) from Northeastern Florida Bay (FCE) from 1996 to Present
The long term goal of the South Florida Water Management District's research program focuses on the following central objective: Regional processes mediated by water flow control population and ecosystem level dynamics at any location within the coastal Everglades landscape. This phenomenon is best exemplified in the dynamics of an estuarine oligohaline zone where fresh water draining phosphorus-limited Everglades marshes mixes with water from the more nitrogen-limited coastal ocean. Data in this data package are specifically to monitor soil elevation change relative to current sea level using the SET method. The Feldspar Marker Horizon method was used in conjunction with the SET data. The Feldspar data are in dataset FCE1213.
Regeneration after Hurricane Hugo, woody species > 10 cm tall (9Ha grid, El Verde) (9Ha Plots Small Data Set)
The purpose of this data set is to document vegetation damage and recovery following Hurricane Hugo, a borderline category 3-4 hurricane with winds from 130 to 160mph (110kts) and a pressure of 945 to 946.1 mb, which hit Puerto Rico in September 18th, 1989 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
PIE LTER, geographic information for the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA.
A description of the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA, USA. The marsh around Clubhead Creek, Rolwey, MA, USA was used as a reference.
PIE LTER, marsh surface marker horizon measurements at each Surface Elevation Table (SET) site, Rowley, MA.
Marker horizon depth measurements at each Surface Elevation Table (SET) site. Marker horizons are commonly used in conjunction with the SET. Marker horizons measure vertical accretion which predominantly incorporates surface processes.
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
Predicting evaporation from mountain streams -- data set
<p>These files constitute the data sets used for the analysis, and generation of figures and tables reported in the manuscript titled "Predicting evaporation from mountain streams" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'.</p>
The Photo Set of the Master Tape of Erkki Kurenniemi's On-Off (1963)
<p>The set includes the photographs of the master tape of Erkki Kurenniemi's first stand alone tape music work <em>On-Off</em> (1963). The tape is located at the University of Helsinki Music Research Laboratory & Electronic Music Studio (UHMRL) tape archive.</p>
Climate reconstructions for the SMPDSv1 modern pollen data set
<p>The dataset contains estimates of three bioclimatic variables at modern pollen sites from the SMPDSv1 modern pollen data set (Harrison, 2019). The bioclimatic variables are mean temperature of the coldest month (MTCO), growing degree days above 0°C (GDD0), and an annual Moisture Index, defined as the ratio of annual precipitation to annual potential evapotranspiration (MI). Estimates of these bioclimatic variables were derived using geographically-weighted regression of gridded climate data in order to correct for elevation differences between each pollen site and the corresponding grid cell. The climatological data (mean monthly temperature, precipitation, and fractional sunshine hours) were derived from the CRU CL v2.0 gridded dataset of modern (1961-1990) surface climate at 10 arc minute resolution (~18 km) (New et al., 2002).Geographically- weighted regression (GWR) was carried out in ArcGIS (v10.3, ESRI, 2014). A fixed bandwidth kernel of 1.06 ° (~140km) was used in the GWR because this optimized model diagnostics and reduced spatial clustering of residuals relative to other bandwidths. The climate of each pollen site was then estimated based on its longitude, latitude, and elevation. MTCO was taken directly from the GWR regression. GDD0 were estimated from daily data using a mean-conserving interpolation of the monthly mean temperatures. MI was calculated for each pollen site using code modified from SPLASH v1.0 (Davis et al., 2017) based on daily values of precipitation, temperature and sunshine hours again obtained using a mean-conserving interpolation of the monthly values of each.</p>
PetrocShelley/Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set: Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set
<p>All data files for the Measured solid state and sub-cooled liquid vapour pressures of nitroaromatics using Knudsen effusion mass spectrometry by Shelley et al.</p>
InterFlex WP3 data set_ SGAM diagrams_interface data base_service identification
<p>This data set contains the InterFlex demonstration use case descriptions in the form of SGAM diagrams as well as the interface data base which was used for different deliverables and the repective results within work package 3 "Impact and deployment analysis of the innovative solutions". There has also been one publication in this regard ( <a href="https://doi.org/10.1109/INDIN.2018.8472053">10.1109/INDIN.2018.8472053</a>)</p> <p>Furthermore, it includes the service mappings for the InterFlex (under GA 731289) demonstrators as an input for different WP3 3.1 subtasks, deliverbale (D3.2) as well as a scientific publication (ICRERA 2019, ID 239, online ISSN: 2572-6013)</p>
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
<p>The problem of training a deep neural network with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. In the image domain, typical FSL applications are those related to face recognition. In the audio domain, music fraud or speaker recognition can be clearly benefited from FSL methods. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research in this area, results with two baseline systems (one trained from scratch and another based on transfer learning), are presented.</p> <p> </p>
RNAPosers: Machine Learning Classifiers For RNA-Ligand Poses [Data Set]
<ul> <li>This dataset contains the decoys poses used to train and test RNAPosers, a set of RNA-ligand pose classifiers.</li> <li>The folder of each RNA-ligand complex (identified using its PDB ID) contains: <ul> <li>Ligand SMILES: lig.smi</li> <li>Ligand coordinate: lig.sd</li> <li>Receptor coordinate: receptor.mol2</li> <li>Pose coordinates: poses.sd</li> <li>Pose similarity data: rmsd.txt</li> </ul> </li> </ul>
A set of allsky camera images from latitude 55 degrees North
<p>A set of R G and B images from an allsky camera situated in Copenhagen, Denmark. The images have been darksubtracted and split into these 16-bit FITS format images. Each image is the sum of something like 9 PNG images which each, originally, were 14 bit images from a ZWO camera cmos detector.</p>
Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set
<p><strong>53 marker MELC Run in human tonsil</strong>. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.</p> <p> </p>
The [CII] 158 μm line emission in high-redshift galaxies: Data Set
<p>This data set contains all data tables associated to the publication: "The [CII] 158 μm line emission in high-redshift galaxies"; A&A Lagache, Cousin, Chatzikos 2018. Please cite it if you use those data.</p>
VICINITY IoT Use Case Data Sets
<p>This repository contains Internet-of-Things (IoT) use case data sets from the VICINITY project. The use cases are described under https://vicinity2020.eu, where also the contributing companies are described in more detail. </p> <p>The use cases include: </p> <ul> <li>eHealth use case data by GNOMON/CERTH (anonymized)</li> <li>Smart parking, building control use case data by HITS, TINYM</li> <li>Energy management data set by ENERC</li> <li>Several other, smaller use cases with less data from open call winners. </li> </ul> <p>The data sets have also been the basis for the publications available for download under the same URL; the folder AAU gives additionally the data from AAU's publications. </p> <p> </p>
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
<p>Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:</p> <ul> <li>51 converted mass spectral files in mzML format</li> <li>An .RData-file containing the extracted ion chromtograms (EICs)</li> <li>The randomly selected subset and the original output table of MZmine in .csv-format</li> <li>Example .xlsx files for the classification</li> <li>2 central classification tables</li> <li>Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs</li> </ul> <p>For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).</p>
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
STROBE checklist for a set of scientific works about COVID-19
<p>STROBE checklist for a set of scientific works about COVID-19. This dataset is the result of the expert-based assessment carried out in <a href="https://arxiv.org/abs/2004.06179">arXiv:2004.06179</a>.</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.