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3,696 results for “references”
Air and soil temperature data from the Reference Stand network at the Andrews Experimental Forest, 1971 to present
The current network of temperature measurement sites are designed to represent spatial variability of air and soil temperature in rugged mountain topography, and serve as second-level stations to capture specific microclimate temperatures in conjunction with a network of Benchmark Meteorological Stations (MS001). The air and soil thermograph network has been reduced from the historical network of 37 sites originally established. Currently there are 10 measurement sites with two of these sites measuring relative humidity in addition to air and soil temperature. An original network of 19 sites (RS01-RS19) were established during the International Biome Program in the early 1970's. Emphasis on phenology, plant moisture stress, and leaf nutrient content led to extending this network of air and soil temperature measurement. A plant community classification system (Dyrness et al., 1971) was used as a primary means of stratification, and a set of permanent vegetation plots (Reference Stands) was installed to represent forest communities with distinct vegetation and hypothesized different environments (Dyrness et al., 1974). A thermograph network was installed within the reference stands in the early 1970's (Zobel et al., 1974), and vegetation standing crop, tree growth and mortality, and plant succession were also measured. The majority of these sites were established to monitor micro-meteorological data under the canopy. The purpose of this network was to provide air and soil temperature data for modeling photosynthesis, respiration, phenology, and decomposition, and to measure environmental gradients.
EATRIS-Plus multi-omics data of a human reference cohort
<p>In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics <br>profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, the following data types are included:</p> <ul> <li>Targeted metabolomics (acylcarnitines, amino acids and very long chain fatty acids)</li> <li>Lipidomics (negative and positive ionization modes)</li> <li>Proteomics</li> <li>mRNA-seq</li> <li>miRNA-seq</li> <li>miRNA qRT-PCR</li> <li>Enzymation Methylation sequencing</li> </ul> <p>The pre-processed mult-omics data can be accessed here in the shape of a MultiAssayExperiment object (<a href="https://doi.org/10.1158/0008-5472.can-17-0344">Ramos et al. 2017</a>). Instructions on how to read the object into R can be found here: <a href="https://github.com/EATRIS/Read_MultiAssayExperiment">Read_MultiAssayExperiment</a>.</p> <p>A similar object for Python (MuData) including the same data will be added later. </p> <p> </p> <p>DATA AVAILABILITY STATEMENT:</p> <p>Full data related to the EATRIS-Plus multiomic cohort are available in the ClinData repository (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclindata.imtm.cz%2F&data=05%7C02%7CCasper.deVisser%40radboudumc.nl%7C347853c763954a30b82208dc3ebe2571%7Cb208fe69471e48c48d87025e9b9a157f%7C0%7C0%7C638454233596610669%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Jp7u%2BXblry9QNg4EQkJE4CKxMZZfMK9U84Ob7E6up90%3D&reserved=0">https://clindata.imtm.cz</a>) and include full phenotypic information, physical and laboratory examinations, multiomic data from white blood cells (whole genome sequencing, enzymatic methylation DNA sequencing, mRNA sequencing, miRNA sequencing) or plasma (miRNA qPCR profiling, proteomics, targeted metabolomics, untargeted lipidomics, Raman spectroscopy profiling). However, access is restricted due to legal, ethical, scientific and/or commercial reasons. Access to the data is subject to approval and a data sharing transfer agreement. For data access please contact <a href="mailto:data.access@imtm.upol.cz">data.access@imtm.cz</a>. </p>
Real-time deformability cytometry reference data
<p>This dataset consists of four exemplary real-time fluorescence and deformability cytometry measurements. The HDF5-files can be opened with dclab [1] or Shape-Out [2].</p> <p><strong>calibration_beads.rtdc</strong><br> The calibartion beads (8 Peaks, PolyAN) consist of eight bead populations with different mixtures of fluorophores.</p> <p><br> <strong>CD34_HSPC.rtdc</strong><br> Hematopoietic stem and progenitor cells (HSPCs) were obtained using apheresis. The cells were tagged with a fluorescently labeled antibody that binds to the CD34 transmembrane protein. CD34-positive HSPCs are gated with `fl3_max > 90`. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> <strong>leukocytes.rtdc</strong><br> The leukocyte population (white blood cells) of this blood sample can be visualized by setting `aspect < 2` and `area_ratio < 1.05`. For more information, see e.g. [4].</p> <p><br> <strong>reticulocytes.rtdc</strong><br> Blood contains mostly red blood cells (RBCs) and about 1% reticulocytes (which develop into mature RBCs). Reticulocytes contain ribosomal RNA which was stained with Syto13 for this measurement. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> [1] <a href="https://github.com/ZellMechanik-Dresden/dclab">https://github.com/ZellMechanik-Dresden/dclab</a></p> <p>[2] <a href="https://github.com/ZellMechanik-Dresden/ShapeOut">https://github.com/ZellMechanik-Dresden/ShapeOut</a></p> <p>[3] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:<a href="https://dx.doi.org/10.1038/nmeth.4639">10.1038/nmeth.4639</a>.</p> <p>[4] Toepfner et al., "Detection of human disease conditions by single-cell morpho-rheological phenotyping of whole blood". eLife, 7:e29213, 2017. doi:<a href="https://dx.doi.org/10.1101/145078">10.1101/145078</a>.</p> <p><br> SHA256 sums:<br> 08c2ef13eed903ef0f9e451727ab8484df09b5d3b39227dab726e0164dcbe244 calibration_beads.rtdc<br> 663b44a9db88d85996500045489e37a317cf115719223a531d617f8e3d450e79 CD34_HSPC.rtdc<br> 68bd538b42ffb990f1db52d5f3b21f37c9aff31208ab284f3910fd6872c40fdb leukocytes.rtdc<br> 5c323ea75bf7eeb2a28d922730772d50270dd872d6957e60d6062663f3628fb3 reticulocytes.rtdc</p>
Seasonal reference harvest measurements of vegetation at 15 net primary production (NPP) study sites at Jornada Basin LTER, 1989-ongoing
This data package contains reference harvest measurements for the long-term Net Primary Production (NPP) study at the Jornada Basin LTER. Data here include horizontal cover, vertical height, and aboveground biomass of plants harvested near, but outside, a grid of permanent NPP quadrats at each of 15 NPP sites. These sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. All sites are excluded from domestic grazing. Eleven sites are in non-grazed pastures, and at the other four sites 1 hectare areas around the observational plots were fenced in 1988. At all sites a grid of 49 (48 at one playa location) 1m x 1m replicate quadrats was laid out when sampling began in 1989. Harvests are made outside the quadrat grid, but inside the fence. Height and cover are recorded in the field. Live biomass is weighed in the lab and all measurements are recorded as reference harvest data. Subsequently, regressions between the harvested biomass and plant volume values are used to derive allometric equations that determine biomass from non-destructive volume measurements in the permanent NPP quadrats. Further details are described in the methods metadata. This is an ongoing study with new harvest data (from selected species) collected in the spring, fall, and winter each year. Attention: These data are not appropriate for estimates of percentage cover because of the way the data are collected.
AET01 Konza prairie grass reference evapotranspiration
Estimated evapotranspiration from a hypothetical short grass with a height of 0.12 m, a surface resistance of 70 s m-1, and an albedo of 0.23 (no water stress). Dataset contains daily total estimated evapotranspiration.
Oyster and associated fauna counts and lengths from restored and reference reefs in the coastal bays of Virginia, 2005-2019
This dataset has been superceded by Lusk, B., R. Smith, and M.C.N. Castorani. 2024. Oyster fauna lengths, counts, and biomass from restored and reference reefs in Virginia coastal bays, 2005-2023 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/d68de69f29cee5f737313a07f813f245 (Accessed 2024-02-22). which includes additional years and parameters. Oyster and associated reef fauna counts and lengths were sampled at 16 natural reference reefs and 61 restored shell plant reefs located at 18 sites in the Virginia Coast Reserve. Overfishing and disease decimated oyster reefs in the Virginia Coast Reserve in the 1900s. Reference reefs were defined as remnant reefs that naturally recovered in the early 2000s to develop the pronounced vertical structure and multiple oyster size classes that represent the desired endpoint of restoration efforts. Nearly every year since 2003, The Nature Conservancy and Virginia Marine Resource Commission have constructed oyster reefs in intertidal areas in the VCR. To construct the restored reefs, practitioners applied dredged, fossilized oyster shell to intertidal locations chosen for their bottom stability and accessibility (locations lacked oysters prior to construction). Whelk shell supplemented the oyster shell at 9 of the restored reefs.
Reference percentiles for carotid measures of subclinical atherosclerosis in children and adolescents – Data from the KiGGS study
<p>The dataset presents reference centile data for carotid intima-media thickness as well as four carotid stiffness parameters. The reference percentiles are based on data from the German Health Interview and Examination Survey for Children and Adolescents 2003-2006 (KiGGS). KiGGS started as a cross-sectional study conducted between 2003 and 2006 which was based on a nationally representative sample and aimed at obtaining comprehensive data on the health of children and adolescents aged 0 to 17 years living in Germany. Detailed information on study design and conduct has been published in peer reviewed journals as well as detailed papers on individual parameters. The documentation provides relevant references.<br> <br> 11 years later, KiGGS Wave 2 (2014-2017) was conducted as an interview and examination survey. Carotid sonography was attempted at follow-up (KiGGS2) in all 4,798 participants of the KiGGS cohort aged 14 to 28 years. Carotid intima-media thickness (CIMT) was successfully measured in 4,709 participants. Reference centiles for the distensibility coefficient, stiffness index ß, Young's elastic modulus and Peterson's elastic modulus were computed using data on 4,305 adolescents and young adults aged 14 to 28.</p>
MMSEQS meets AntiRef: reference clusters of human antibody sequences
<p>This data set contains pre-computed mmseqs databases for the antiref fasta files created by <em>Briney et al.</em> </p> <p>Please cite the original work if you use any of the databases provided here.</p> <p>Sources:</p> <ul> <li><a href="https://github.com/brineylab/antiref">Antiref GitHub</a></li> <li><a href="../records/7474336">Antiref Zenodo</a></li> <li><a href="https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad109/7247530?login=true">Antiref Paper</a></li> </ul> <p> </p> <p>The mmseqs databases were created as follows:</p> <p> </p> <p>```</p> <p>aria2x -x16 -s16 --input-file antiref_links.txt<br>snakemake -s antiref_mmseqs.smk --jobs 1 --cores 1 --local-cores 250</p> <p>```</p>
Protein haplotype sequences obtained by ProHap from the Haplotype Reference Consortium Release 1.1 dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Haplotype Reference Consortium, Release 1.1 (<a href="https://ega-archive.org/datasets/EGAD00001002729" target="_blank" rel="noopener">https://ega-archive.org/datasets/EGAD00001002729</a>). We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>Release 1.1 of the HRC is provided aligned with the GRCh37 reference genome. We have performed a liftover to the GRCh38 reference using GeneBe (https://genebe.net/tools/liftover). Variants for which the reported alternative allele is considered as reference in GRCh38 were removed. A threshold of 1% minor allele frequency was applied to filter the remaining variants. After translation, a frequency threshold of 0.5% was applied to filter the resulting unique non-canonical sequences. The complete configuration file for the ProHap run is attached to this repository.</p> <p>This dataset contains one compressed directory, contains the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>The file is provided in two formats - full and simplified. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Multi-organ Abdominal CT Reference Standard Segmentations
<p>DenseVNet Multi-organ Segmentation on Abdominal CT</p> <p>This dataset includes the multi-organ abdominal CT reference segmentations publicly released in conjunction with the IEEE Transactions on Medical Imaging paper "Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks" <a href="#1">[1]</a>.</p> <p>The data comprises reference segmentations for 90 abdominal CT images delineating multiple organs: the spleen, left kidney, gallbladder, esophagus, liver, stomach, pancreas and duodenum.</p> <p>The abdominal CT images and some of the reference segmentations were drawn from two data sets: <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">The Cancer Image Archive (TCIA) Pancreas-CT data set</a> [<a href="#2">2</a>-<a href="#4">4</a>] and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> [<a href="#5">5</a>-<a href="#6">6</a>]. The Pancreas-CT data set comprises abdominal CT acquired at the National Institutes of Health Clinical Center from pre-nephrectomy healthy kidney donors or patients with neither major abdominal pathologies nor pancreatic cancer lesions. Segmentations of the pancreas are included with this data set; images were manually labeled slice-by-slice by a medical student, and verified/modified by an experienced radiologist. The BTCV data set comprises abdominal CT acquired at the Vanderbilt University Medical Center from metastatic liver cancer patients or post-operative ventral hernia patients. Segmentations of the spleen, right and left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, portal vein and splenic vein, pancreas, right adrenal gland, left adrenal gland are included in this data set; images were manually labeled by two experienced undergraduate students, and verified by a radiologist on a volumetric basis using the MIPAV software.</p> <p>Segmentations that were not present in the original data sets were performed interactively using Matlab 2015b and ITK-SNAP 3.2 by an image research fellow under the supervision of a board-certified radiologist with 8 years of experience in gastrointestinal CT and MRI image interpretation. Segmentations that were present in the original data sets were edited to ensure a consistent segmentation protocol across the data set.</p> <p>Terms of use</p> <p>The terms of use of this data set include the terms of use of both the <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">TCIA Pancreas-CT data set</a> (see tabs for data links and terms of use) and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> (<a href="https://doi.org/10.7303/syn3193805">terms of use</a>; after <a href="https://www.synapse.org/#!Synapse:syn3193805/wiki/217753">registration</a>, you can <a href="https://www.synapse.org/#!Synapse:syn3376386">access the data</a>). If you use these reference segmentations, please cite the above manuscript and the references below. Because these data include manual segmentations of images from the Beyond the Cranial Vault challenge test data, they may not be used to develop submissions for the challenge.</p> <p>References</p> <p>[1] Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Transactions on Medical Imaging, 2018.</p> <p>[2] Roth HR, Farag A, Turkbey EB, Lu L, Liu J, and Summers RM. (2016). Data From Pancreas-CT. The Cancer Imaging Archive. <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU</a></p> <p>[3] Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. <a href="http://arxiv.org/pdf/1506.06448.pdf">http://arxiv.org/pdf/1506.06448.pdf</a></p> <p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. <a href="http://doi.org/10.1007/s10278-013-9622-7">http://doi.org/10.1007/s10278-013-9622-7</a></p> <p>[5] Xu Z, Lee CP, Heinrich MP, Modat M, Rueckert D, Ourselin S, Abramson RG, and Landman BA, "Evaluation of six registration methods for the human abdomen on clinically acquired CT," IEEE Trans. Biomed. Eng., vol. 63, no. 8, pp. 1563–1572, 2016.<a href="http://doi.org/10.1109/TBME.2016.2574816">http://doi.org/10.1109/TBME.2016.2574816</a></p> <p>[6] Landman BA, Xu Z, Igelsias JE, Styner M, Langerak TR, and Klein A, "MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge," 2015, <a href="https://doi.org/10.7303/syn3193805">https://doi.org/10.7303/syn3193805</a></p> <p>File format Labels are in NIfTI format with the following label definitions. Labels marked with * are only available in the BTCV data set.</p> <ol> <li>spleen</li> <li>right kidney*</li> <li>left kidney</li> <li>gallbladder</li> <li>esophagus</li> <li>liver</li> <li>stomach</li> <li>aorta*</li> <li>inferior vena cava*</li> <li>portal vein and splenic vein*</li> <li>pancreas</li> <li>right adrenal gland*</li> <li>left adrenal gland*</li> <li>duodenum</li> </ol> <p>Subjects included in the dataset</p> <p>The data comprises segmentation volumes for 90 cases, and the cropping coordinates (cropping.csv) used in the manuscript. The abdominal CT can be obtained from the links above. The reference standard segmentations may be incomplete outside of the specified cropping region. The cases are listed by their subject identifiers in their original data set:</p> <p> </p> <p><span class="math-tex">\(\begin{bmatrix} 1 & TCIA & Pancreas-CT & 0002\\ 2 & TCIA & Pancreas-CT & 0003\\ 3 & TCIA & Pancreas-CT & 0004\\ 4 & TCIA & Pancreas-CT & 0005\\ 5 & TCIA & Pancreas-CT & 0006\\ 6 & TCIA & Pancreas-CT & 0007\\ 7 & TCIA & Pancreas-CT & 0008\\ 8 & TCIA & Pancreas-CT & 0009\\ 9 & TCIA & Pancreas-CT & 0010\\ 10 & TCIA & Pancreas-CT & 0011\\ 11 & TCIA & Pancreas-CT & 0012\\ 12 & TCIA & Pancreas-CT & 0013\\ 13 & TCIA & Pancreas-CT & 0014\\ 14 & TCIA & Pancreas-CT & 0016\\ 15 & TCIA & Pancreas-CT & 0017\\ 16 & TCIA & Pancreas-CT & 0018\\ 17 & TCIA & Pancreas-CT & 0019\\ 18 & TCIA & Pancreas-CT & 0020\\ 19 & TCIA & Pancreas-CT & 0021\\ 20 & TCIA & Pancreas-CT & 0022\\ 21 & TCIA & Pancreas-CT & 0024\\ 22 & TCIA & Pancreas-CT & 0025\\ 23 & TCIA & Pancreas-CT & 0026\\ 24 & TCIA & Pancreas-CT & 0027\\ 25 & TCIA & Pancreas-CT & 0028\\ 26 & TCIA & Pancreas-CT & 0029\\ 27 & TCIA & Pancreas-CT & 0030\\ 28 & TCIA & Pancreas-CT & 0031\\ 29 & TCIA & Pancreas-CT & 0032\\ 30 & TCIA & Pancreas-CT & 0033\\ 31 & TCIA & Pancreas-CT & 0034\\ 32 & TCIA & Pancreas-CT & 0035\\ 33 & TCIA & Pancreas-CT & 0038\\ 34 & TCIA & Pancreas-CT & 0039\\ 35 & TCIA & Pancreas-CT & 0040\\ 36 & TCIA & Pancreas-CT & 0041\\ 37 & TCIA & Pancreas-CT & 0042\\ 38 & TCIA & Pancreas-CT & 0043\\ 39 & TCIA & Pancreas-CT & 0044\\ 40 & TCIA & Pancreas-CT & 0045\\ 41 & TCIA & Pancreas-CT & 0046\\ 42 & TCIA & Pancreas-CT & 0047\\ 43 & TCIA & Pancreas-CT & 0048\\ 44 & Synapse & BeyondTheCranialVault & 0001\\ 45 & Synapse & BeyondTheCranialVault & 0002\\ 46 & Synapse & BeyondTheCranialVault & 0003\\ 47 & Synapse & BeyondTheCranialVault & 0004\\ 48 & Synapse & BeyondTheCranialVault & 0005\\ 49 & Synapse & BeyondTheCranialVault & 0006\\ 50 & Synapse & BeyondTheCranialVault & 0007\\ 51 & Synapse & BeyondTheCranialVault & 0008\\ 52 & Synapse & BeyondTheCranialVault & 0009\\ 53 & Synapse & BeyondTheCranialVault & 0010\\ 54 & Synapse & BeyondTheCranialVault & 0021\\ 55 & Synapse & BeyondTheCranialVault & 0022\\ 56 & Synapse & BeyondTheCranialVault & 0023\\ 57 & Synapse & BeyondTheCranialVault & 0024\\ 58 & Synapse & BeyondTheCranialVault & 0025\\ 59 & Synapse & BeyondTheCranialVault & 0026\\ 60 & Synapse & BeyondTheCranialVault & 0027\\ 61 & Synapse & BeyondTheCranialVault & 0028\\ 62 & Synapse & BeyondTheCranialVault & 0029\\ 63 & Synapse & BeyondTheCranialVault & 0030\\ 64 & Synapse & BeyondTheCranialVault & 0031\\ 65 & Synapse & BeyondTheCranialVault & 0032\\ 66 & Synapse & BeyondTheCranialVault & 0033\\ 67 & Synapse & BeyondTheCranialVault & 0034\\ 68 & Synapse & BeyondTheCranialVault & 0035\\ 69 & Synapse & BeyondTheCranialVault & 0036\\ 70 & Synapse & BeyondTheCranialVault & 0037\\ 71 & Synapse & BeyondTheCranialVault & 0038\\ 72 & Synapse & BeyondTheCranialVault & 0039\\ 73 & Synapse & BeyondTheCranialVault & 0040\\ 74 & Synapse & BeyondTheCranialVault & 0061\\ 75 & Synapse & BeyondTheCranialVault & 0062\\ 76 & Synapse & BeyondTheCranialVault & 0063\\ 77 & Synapse & BeyondTheCranialVault & 0064\\ 78 & Synapse & BeyondTheCranialVault & 0065\\ 79 & Synapse & BeyondTheCranialVault & 0066\\ 80 & Synapse & BeyondTheCranialVault & 0067\\ 81 & Synapse & BeyondTheCranialVault & 0068\\ 82 & Synapse & BeyondTheCranialVault & 0069\\ 83 & Synapse & BeyondTheCranialVault & 0070\\ 84 & Synapse & BeyondTheCranialVault & 0074\\ 85 & Synapse & BeyondTheCranialVault & 0075\\ 86 & Synapse & BeyondTheCranialVault & 0076\\ 87 & Synapse & BeyondTheCranialVault & 0077\\ 88 & Synapse & BeyondTheCranialVault & 0078\\ 89 & Synapse & BeyondTheCranialVault & 0079\\ 90 & Synapse & BeyondTheCranialVault & 0080\\ \end{bmatrix}\)</span></p>
Reference data and documentation for Skills4EOSC Deliverable D6.1 Mapping of existing professional networks
<p>This record presents the data underlying <strong>Skills4EOSC Deliverable D6.1 Mapping of existing professional networks</strong> and relevant documentation of the search string.</p>
Invertebrate Community Asemblage from the Arctic LTER Upper Kuparuk River Reference (2001-2012) and Fertilized Reach (2002-2016), Toolik Field Station, Alaska
Surber sampler (25 X 25 cm frame fitted with a 243 um mesh net) was used to sample invertebrates at on the Kuparuk River in Reference (2001-2012) and Fertilized Reach (2002-2016) reach.
May to July 2018 regions of interest (ROIs) of tidal marsh and tidal forest plant species to be used as ground reference data in habitat mapping
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground reference data for habitat mapping. Regions of interest (ROIs) for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were generated near ground control points (GCP) by digitizing vegetation areas in ArcGIS 10.4 based on field maps.These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
SBC LTER: Reference: Sea-surface water temperature, Santa Barbara Harbor, Santa Barbara, CA, USA, 1955 to present, ongoing
The SBC-LTER has access to data on seawater temperature collected at Santa Barbara Harbor, Santa Barbara, CA, USA through the Scripps Institution of Oceanography Manual Shore Stations program. The SIO Manual Shore Stations program provides data and information about this shore station. For further information, please visit the SIO Manual Shore Stations website at https://library.ucsd.edu/dc/object/bb07606686. Please note: manual shore station data is updated periodically, not continuously. Funding for the Shore Stations Program provided by the California Department of Parks and Recreation, Natural Resources Division, Award# C22820005. Contact shorestation@ucsd.edu if you have questions
Oyster fauna lengths, counts, and biomass from restored and reference reefs in Virginia coastal bays, 2005-2023
Oyster reef fauna counts and lengths were sampled at natural "reference" reefs and restored shell plant reefs located in the Virginia Coast Reserve. Overfishing and disease decimated oyster reefs in the Virginia Coast Reserve in the 1900s. Reference reefs were defined as remnant reefs that naturally recovered in the early 2000s to develop the pronounced vertical structure and multiple oyster size classes that represent the desired endpoint of restoration efforts. Nearly every year since 2003, The Nature Conservancy and Virginia Marine Resource Commission have constructed oyster reefs in intertidal areas in the VCR. To construct the restored reefs, practitioners launched dredged, fossilized oyster shell from barges to intertidal locations chosen for their bottom stability and accessibility (locations lacked oysters prior to construction). Whelk shell supplemented the oyster shell at some of the restored reefs. TNC practitioners monitor select restored and reference reefs annually for adult and spat live oysters, adult and spat box oysters, mud crabs, mud snails, oyster drills, live clams, and mussels.
Reference Windfarm database CNk4 90
<p>Dataset for TotalControl reference windfarm database simulation of a conventionally neutral boundary layer flow with 90 degree inflow wind direction angle (Casename CNk4 90)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
IPBES Data Management Tutorials - Session 5.5: References and citation manager: Zotero
<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> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session, <em>References and citation manager: Zotero, </em>reviews why IPBES recommends Zotero to manage references and provides links to key resources.</p>
BAM reference data: XPS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a)
<p>The raw data are given as VAMAS-File. The measurement condtions are given in the file. The C1s-fits are provided as ascii-files.</p> <p>For further information please look at Radnik, J. Kersting, R., Hagenhoff, B., Bennet, F., Ciornii, D.; Nymark, P., Grafström R. and Hodoroaba, V.-D. <em>Nanomaterials </em><strong>2021</strong>, <em>11</em>, 639. https://doi.org/10.3390/nano11030639.</p> <p>The transmission function is obtained as ascii-file trm.dat. The energy scale is kinetic energy.</p> <p> </p> <p>Measurement conditions:</p> <p>XPS measurements were performed at an Axis Ultra DLD (KRATOS, Manchester, UK) with monochromatic Al K radiation (E = 1486.6 eV). The electron emission angle was 0° and the source-to-analyzer angle was 60°. The binding energy scale of the instrument was calibrated following a Kratos analytical procedure, which uses ISO 15472 binding energy data. The setting of the instrument was the hybrid lens mode and the slot mode with an analysis area of approximately 300x700 m². Furthermore, charge neutralization with a flood gun was used. All spectra were recorded in the fixed analyzer transmission (FAT) mode. The samples were measured as powders prepared on a special stainless-steel sample holder.</p> <p> </p> <p> </p> <p> </p>
Values of the reference prior for the Poi(s+b) model from JINST 7 (2012) P01012
<p>Plain text table with the values of the reference prior π(s) for the Poi(s+b) model used in the statistical inference about counting experiments, as explained in JINST 7 (2012) P01012, doi:10.1088/1748-0221/7/01/P01012, http://arxiv.org/abs/1108.4270. The values are useful to find approximate expressions which are quicker to compute than the original prior, as explained in http://arxiv.org/abs/1407.5893 (where this dataset is referred to).</p> <p>Each line is a sequence of spaces-separated values, and the file can be considered a table. The first line starts with two strings "shape" and "rate" which represent the titles of the corresponding columns in the data table. They refer to the shape and rate parameters defining the background prior. Next, N signal values starting from s=0 to s=70 are reported. They are the values at which π(s) is computed for any subsequent line.</p> <p>Starting from the second line, the format is always the same. The first two values are the shape and rate parameters defining the background prior used to compute π(s) in this line. Next, the N values π(s=0), ..., π(s=70) are reported. As π(0) = 1, the third column is constant (it might be useful to debug the data reading).</p> <p>As explained in http://arxiv.org/abs/1108.4270, simple functional forms may be used to fit the N points (s, π(s)). As the shape and rate parameters from the user's application may be different from those reported in this table, the following procedure shall give a very good approximation to π(s). In the (log(shape), log(rate)) parameters space, locate the neighboring points to the user's background parameter values (in log-log scale). Then interpolate each of the π(s) values to obtain a set of N values (a linear interpolation in log-log scale shall be sufficient). Finally, fit these interpolated values to find the reference prior for the user's application.</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.