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3,843 results for “beta”
Pawpaws prevent predictability: A locally-dominant tree alters understory beta-diversity and community assembly
<p>Data used in "Pawpaws Prevent Predictability: A locally-dominant tree alters understory beta-diversity and community assembly" (Wassel and Myers) accepted for publication in Ecosphere.<br><br><strong>Metadata for Zenodo.pdf </strong>contains more information on the following data files including descriptions of the columns. </p> <p>The file <strong>understory_abundance_data2021.csv</strong> contains all species abundances in 1x1m plots. This data was used for analyses in publication. Each row is a plot, each column is a speceis or plot descriptor, values for columns 5 and higher are species abundances. Data was collected July-August 2021 by Anna Wassel in Missouri, USA. </p> <p>The file <strong>understory_species_list2021.csv </strong>contains a list of the species codes used in the first file with their scientific names and their status as herbs or woody. This was used to filter out herbaceous species from the data set for herbaceous-only analyses. </p> <p> </p>
APEX beta greenhouse gas flux data from both the permafrost plateau area and the active thaw margin, from 2017 on
This dataset contains greenhouse gas flux data for the bog and permafrost plateau sites at the Alaskan Peatland Experiment (APEX) from 2017 on. Includes some environmentals, raw gas concentrations, and derived gas flux rates.
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
Supplemental data for characterization of alpha and beta interactions using the HeXe setup [Eur. Phys. J. C 82, 361]
<p>Repository with supplemental data to:<br> <strong>Characterization of alpha and beta interactions in liquid xenon</strong>. Jörg, F., Cichon, D., Eurin, G. <em>et al. Eur. Phys. J. C</em> <strong>82, </strong>361 (2022) <a href="https://doi.org/10.1140/epjc/s10052-022-10259-3">10.1140/epjc/s10052-022-10259-3</a><br> A pre-print of the article is available <em>on arXiv: </em><a href="http://arxiv.org/abs/2109.13735">2109.13735</a></p> <p><strong>Note: </strong>When re-using the data, please make sure to cite the article (and not only the dataset)</p> <p><br> The files contain the measured data points (as well as their statistical and systematic uncertainties) as shown in the publication.<br> All datasets are stored in the .csv format.</p> <ul> <li><strong>20210924_yields_hexe_kr83m.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>83m</sup>Kr source. The data is shown in Figure 16 (dots) of the publication. Furthermore the file contains the LY ratio between the two Isomeric transitions of the <sup>83m</sup>Kr source, shown in Figure 17 of the article.</li> <li><strong>20210924_yields_hexe_rn222.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>222</sup>Rn source. The data is shown in Figure 18 (blue-ish points) of the publication</li> <li><strong>20210924_drift_velocity_hexe_rn222.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>222</sup>Rn source. Drift velocity is given in units of mm/µs and the datapoints are shown in Figure 20 (black dots) of the publication </li> <li><strong>20210924_drift_velocity_hexe_kr83m.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>83m</sup>Kr source. Drift velocity is given in units of mm/µs and are not displayed in the publications due to visibility reasons.</li> </ul> <p><strong>Minimum working example to plot the drift velocity using the <sup>83m</sup>Kr data:</strong></p> <pre><code class="language-python"> 1 import numpy as np 2 import matplotlib.pyplot as plt 3 4 # load the data set 5 data = np.loadtxt("20220427_drift_velocity_hexe_kr83m.csv", delimiter=",") 6 7 # Plot the systematic uncertainty on the drift field 8 plt.errorbar(data[:,0], data[:,2], xerr=data[:,1], fmt="o", capsize=2, ecolor="darkgray", 9 alpha=0.7, elinewidth=3, color="black") 10 11 # Plot the actual data points 12 plt.errorbar(data[:,0], data[:,2], yerr=data[:,3], fmt="o", color="black") 13 14 # Label the axis and define the range 15 plt.ylabel("Drift Velocity [mm/µs]") 16 plt.xlabel("Drift Field [kV/cm]") 17 plt.xscale("log") 18 plt.xlim(0.006, 2) 19 plt.ylim(0, 2.4) 20 plt.show() </code></pre> <p> </p>
Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines
<p><strong>SUMMARY OF THE STUDY</strong></p> <p>We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk.</p> <p><strong>DESCRIPTION OF FILES:</strong></p> <ul> <li>Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853</li> <li>Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853</li> <li>Supplementary Data 6. Variants selected for SNP-SELEX assay </li> <li>Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX</li> <li>Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853</li> <li>Supplementary Data 9. CRISPR-KO screen results - also in GSE205853</li> <li>Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853</li> <li>Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 18. Luciferase assay rs10483809</li> <li>Supplementary Data 19. SOCS1 knockdown qPCR results</li> <li>Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results</li> </ul> <p><strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong></p> <p><em>Please refer to publication and GEO for details on methods.</em></p>
Measurement of the bound-state beta decay of 205Tl(81+): intermediate and result data
<p>The data presented here is the intermediate and result data from the measurement of the bound-state beta decay of 205Tl(81+), experiment G-20-0E121, which was performed at the Experimental Storage Ring (ESR) at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt (Germany) in the frame of FAIR Phase-0. The experimental measurement was done from the 26th March 2020 to 6th April 2020.</p> <p><strong>Intermediate Data:</strong> During the experiment, the ESR monitored the beam via three main detectors: the non-destructive 245 MHz Schottky resonator, the DC Current Transformer (DCCT), and a Multi-Wire Proportional Chamber (MWPC). In particular:</p> <ol> <li>Schottky data: the integrated Schottky noise power density for the 205Tl(81+) peak and the 205Pb(82+) peak is provided for each storage measurement. The full Schottky spectrum can be made available on request. As described in the related works below, the Schottky data became saturated above a certain threshold due to a mismatched amplifier in the NTCAP DAQ. This results in non-exponential decay of Schottky peaks for high intensities. </li> <li>DCCT data: the entire beam current in the ring was monitored using the DCCT. The DCCT was recorded using a scalar counter and thus has a non-zero offset value, which we determined to be 28.199 µA from a period with no beam. The DCCT is intended to be used as a diagnostic tool, and is not as precise as other, purpose-built detectors.</li> <li>MWPC data: for most storage measurements, a MWPC detector was placed downstream of the gas target on the outside of the ring to detect electron recombination products. The provided data is the detection rate on the anode.</li> <li>The gas target density, as recorder by a scaler counter, is also included.</li> </ol> <p>The intermediate data on all three of these detectors plus the gas target density is provided in the tar.gz repository, with individual ROOT files for each storage time. The ROOT files have the naming format "Storage time_MMDD_HH.root". Each file contains 5 TGraph objects.</p> <p><strong>Result Data: </strong>The result data provides all the necessary individual measurement and correction values to extract a bound-state beta-decay rate from the corrected ratios. It is provided in two forms:</p> <ol> <li>BSBD_205Tl-result_data.ods is an ODS table for easy visualisation.</li> <li>BSBD_205Tl-final_vals.txt is a text file used by the Monte Carlo analysis script provided in <a href="https://doi.org/10.5281/zenodo.11560338" target="_blank" rel="noopener">DOI 10.5281/zenodo.11560338</a>.</li> </ol> <p>The "Ratio" column is the corrected 205Pb/205Tl decay ratio for each storage, following Equation (1) of <a href="https://www.nature.com/articles/s41586-024-08130-4" target="_blank" rel="noopener">Leckenby et al. (2024) Nature 635:321–326</a>. 1 sigma error bars, both including and not-including the estimated contamination variation, are provided.</p> <p>Please refer to the related works below or contact the authors for more details.</p>
Github commit data for the article "Beyond Zipf's law: Exploring the discrete generalized beta distribution in open-source repositories"
<p><span>This dataframe corresponds to the data used in the Nowak's et al. 2024 article "Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories" (see reference below).</span></p> <p><span>It consists of the distirbutions of number of commits per user across a number of GitHub repositories. <br><br>There are three columns:</span></p> <ul> <li><span>repository: the repository name</span></li> <li><span># of commits: the number of commits of a given individual</span></li> <li><span>rank: the user rank in the repository (by decreasing number of commits)<br><br></span></li> </ul> <p><strong><span>Reference:</span></strong></p> <p><span>Nowak, P., Santolini, M., Singh, C., Siudem, G., & Tupikina, L. (2024). Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories. <em>Physica A: Statistical Mechanics and Its Applications</em>, <em>649</em>, 129927. <a href="https://doi.org/10.1016/j.physa.2024.129927">https://doi.org/10.1016/j.physa.2024.129927</a></span></p>
Lab513/Optogenetic control of beta-carotene bioproduction in yeast across multiple lab-scales
<p>Dataset related to the article : S. Pouzet et al., Optogenetic control of beta-carotene bioproduction in yeast across multiple lab-scales, 2022. </p> <p>See also the article on BioRXiv : https://www.biorxiv.org/content/10.1101/2022.10.31.514479v1</p> <p>See also the GitHub repository : https://github.com/Lab513/DIY_Optogenetics</p>
APEX beta vegetation surveys from both the permafrost plateau area and the active thaw margin, from 2017 to present
This dataset contains the vegetation survey data for the bog and permafrost plateau sites at the Alaskan Peatland Experiment (APEX) from 2018 to 2024. Each year of surveys is recorded in a separate subsheet.
APEX beta environmentals from both the permafrost plateau area and the active thaw margin, from 2017 to 2019
This dataset contains the environmental data for the bog and permafrost plateau sites at the Alaskan Peatland Experiment (APEX) from 2017 to 2019. Includes summarized flux and vegetation values as well for ease of use in analysis.
4prim-O-beta-D-apiosyl_glucomatronaline
<p>NMR Data of 4'-O-beta-D-apiosyl glucomatronaline</p> <p>1 1D 1H<br> 3 2D 1H-1H COSY<br> 4 2D 1H-13C HSQC<br> 5 2D 1H-13C HMBC<br> 6 2D 1H-1H ROESY<br> 102 1D 13C{1H}</p> <p> </p>
beta-diketones
<p>This data set was used to examine the reactive dynamics and<br> spectroscopic properties of three beta-diketones: malonaldehyde (MA,<br> propandial), acetoacetaldehyde (AAA, 3- oxobutanal) and acetylacetone<br> (AcAc, pentan-2,4-dion), in a neural network based manner. The data<br> set contains different geometries for the three molecules as well as<br> for substructures (Amons [1]).</p> <p>In total the data set contains 71208 geometries with reference<br> energies, forces and dipole moments calculated at the MP2/aug-cc-pVTZ<br> level of theory calculated using the MOLPRO 2018.2.0 [2].</p> <p>For more details, see https://arxiv.org/abs/1911.09475</p> <p>[1] Huang, B.; von Lilienfeld, O.A., arXiv:1707.04146<br> [2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schütz, M.; et al.<br> https://www.molpro.net, 2018.2.0</p>
The Cassandra retrotransposon landscape in sugar beet (Beta vulgaris): Recombination and re-shuffling leads to a high structural variability
<p>Here we provide supplementary data for our study of non-autonomous Cassandra terminal-repeat retrotransposons in miniature (TRIMs) in sugar beet and related genomes.</p> <p>Cassandra sequences are distributed across the plant kingdom and share a unique feature: conserved 5S rDNA promoter motifs within their long terminal repeats (LTRs). This dataset contains two multiple sequence alignments and a sequence list of tandemly-arranged (TA) Cassandra sequences in FASTA format. Alignments cover LTR and internal regions of all Amaranthaceae Cassandra (Ama-Cassandra) from our study. This includes Cassandra full-length sequences from <em>B. vulgaris</em> (Ama_Cassandra_Beet_full-length) and <em>C. quinoa</em> (Ama_Cassandra_Quinoa_full-length). Sequence names include information on host plant, subfamily classification, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation. For the tandemly-arranged Cassandra sequences from sugar beet, we provide a sequence list of twelve sequences (Ama_Cassandra_TA_Beet_list). Here, sequence names refer to TA copy number, host, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation.</p> <p>All sequences were identified in the recent genome assemblys of <em>B. vulgaris</em> (RefBeet1.2; Dohm <em>et al</em>. 2014) and <em>C. quinoa</em> (ASM168347v1; Jarvis <em>et al</em>., 2017).</p>
Fast MLE and Supervised Classification for the Beta-Liouville Multinomial -- Gold Standard Data
<p>Gold standard datasets used in the publication Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial. Datasets were prepared by Cardoso-Cachopo (2007).</p>
Beta-lactamase Trajectory Data
<p>Processed Beta-lactamase trajectories from Gromacs. PBC corrected, alpha carbon only. Matrix produced from JEDi software. Used in related work. </p>
Diffraction images of crystals of the Calx-beta domain of integrin beta4 (PDB code 3FQ4)
<p>Diffraction images of native crystals of the Calx-β domain<br> of the human integrin β4 subunit. Images correspond to the dataset used to solve and refine the pdb entry 3FQ4 (http://www.rcsb.org/pdb/explore/explore.do?structureId=3FQ4).</p> <p>Data was collected using a rotating anode generator (MicrostarH, Bruker AXS) and a mar345dtb image plate detector (marXperts GmbH). The dataset consists of 360 images (1 degree oscillation per image). Data extend to 1.48 Å resolution.</p>
Supplementary material for "High semi-natural vegetation cover and heterogeneity of field sizes promote bird beta-diversity at larger scales in Ethiopian Highlands"
<p><strong>Abstract</strong></p> <ol> <li>The intensification of farming practices exerts detrimental effects on biodiversity. Most research has focused on declines in species richness at local scales (alpha-diversity) although species loss is exacerbated by biotic homogenization that operates at larger scales (i.e., affecting beta-diversity). The majority of studies have been conducted in temperate, industrialized countries while tropical areas remain poorly studied. Agricultural landscapes of sub-Saharan Africa are still largely dominated by small-scale subsistence farming, but strenuous efforts to intensify farming practices are currently spreading to meet a growing food demand. It is therefore crucial to understand how these intensified practices affect biodiversity to mitigate their negative impacts. </li> <li>We investigated how farming system (small- vs large-scale farming) and landscape complexity (semi-natural vegetation cover) drive bird species composition, community turnover, and beta-diversity patterns in Ethiopian Highlands’ agroecosystems. We evaluated the following hypotheses: (1) large-scale farming homogenizes bird communities, (2) community turnover is higher in small-scale farms, (3) interactive effects between landscape complexity and farming systems shape avian communities, (4) heterogeneity of field sizes increases community turnover at larger scales. </li> <li>Bird communities underwent greater compositional changes along the landscape complexity than along the agricultural intensity gradient. Contrary to our expectations, beta-diversity was not significantly lower within large-scale farms (no biotic homogenization), and complex landscapes that still offer a high amount of semi-natural vegetation promoted community turnover in both farming systems. </li> <li>Semi-natural vegetation cover mediated how avian communities responded to agricultural intensification: the compositional differences between small- and large-scale farms increased with vegetation cover, further promoting avian community heterogeneity at the landscape level.</li> <li>The heterogeneity in field sizes also enhanced bird community turnover, suggesting that a combination of both small- and large-scale farming systems within a given landscape unit would promote beta-diversity at larger scales, provided large-scale farms do not become dominant.</li> <li>Synthesis and applications: Landscape complexity shaped avian communities to a stronger degree than farming intensity, emphasizing the importance of semi-natural vegetation and landscape heterogeneity for the maintenance of diverse bird communities and for achieving multifunctional landscapes promoting biodiversity and associated ecosystem services on the High Ethiopian plateaus. <br> </li> </ol>
Control T-cell receptor (TCR) alpha and beta chain nucleotide and amino acid sequences from human and mouse
<p>A dataset of pooled T-cell receptor (TCR) sequences for TCR alpha and beta chains of human and mouse.</p> <p>Sequences are obtained from various samples of healthy individuals/mice using our conventional protocols: see for example [Britanova et al "Dynamics of individual T cell repertoires: from cord blood to centenarians" The Journal of Immunology 2016] and [Izraelson et al. "Comparative analysis of murine T‐cell receptor repertoires." Immunology 2018].</p> <p>The sequences are stored as gzipped clonotype tables in VDJtools format, see [https://vdjtools-doc.readthedocs.io/en/master/input.html#vdjtools-format].</p> <p>This control dataset can be used as a proxy for a generative VDJ rearrangement model to estimate the expected frequency distribution of TCRs and check for enrichment of rare TCR clonotypes and groups of similar TCR sequences. For the implementation of the enrichment analysis, please see CalcDegreeStats routine from VDJtools software, see [https://vdjtools-doc.readthedocs.io/en/master/annotate.html#calcdegreestats].</p> <p>Files named "human.tra.strict.txt.gz", etc are pools of random/naive TCR clonotypes containing unique V/J/CDR3 nucleotide sequence combinations observed in data. The pools.zip file is used for TCR motif inference in VDJdb database [https://github.com/antigenomics/vdjdb-motifs], it contains human.tra.aa.txt, etc files that contain random/naive TCR clonotypes grouped by CDR3 amino acid sequence with the most frequent representative V and J.</p>
Data and script: Reduced enumeration effort, but not coarse taxonomic resolution, is sufficient to represent beta diversity patterns of stream benthic diatoms
<p>This is a dataset on benthic diatom communities sampled in 90 riffles (the local communities) within nine near-pristine subtropical streams (each stream represented a metacommunity) in southeast subtropical Brazil. </p> <p>In addition to the dataset, we also provide the R code used to investigate whether reduced enumeration efforts (i.e., subsets of counted valves per sample) and the identification to the genus level are sufficient to recover patterns in the species composition and in beta diversity of benthic diatom metacommunities.</p>
EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Zr-2.5Nb Alloy
<p>A set of ex-situ electron backscatter diffraction (EBSD) datafiles following rolling of a Zr-2.5Nb alloy at different temperatures (700C, 750C, 775C, 800C, 825C, 850C, 900C) and rolling reductions (50%, 75%, 87.5%). Data for annealing of the material (750C for 2 hours) and following rolling at 800C from a different ‘as-forged’ starting texture is also included. Note, phases in the ctf files marked as Titanium Cubic refer to measurement of the Zirconium Cubic phase. </p> <p>The data in the 'Beta ctf' folder includes a reconstruction of the high temperature beta-phase orientations where possible, reconstructed from the alpha phase orientations using a software based on the Burgers relationship.</p> <p>Please see the accompanying paper for the interpretation of crystallographic texture changes;</p> <p>C.S. Daniel, P.D. Honniball, L. Bradley, M. Preuss, J. Quinta da Fonseca, A detailed study of texture changes during alpha–beta processing of a zirconium alloy, J. Alloys Compd. 804 (2019) 65–83, <a href="https://doi.org/10.1016/j.jallcom.2019.06.338">10.1016/j.jallcom.2019.06.338</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.