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9,277 results for “ETS”
AOSLO Single Cell Blood Flow - Raw Data (eLife paper: Joseph et al. 2019)
<p>Raw AOSLO videos of eLife paper Joseph et al. 2019 - 'Imaging single-cell blood flow in the smallest to largest vessels in the living retina'. <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.7554_eLife.45077&d=DwMFaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=EdsTL7DuEvOHun7eVBmBd9sxUuPhDEmdFDf0tlkKUO4&m=56MyNrE_d-v6PSU7Go9NePVOrIpAvWHBaC8wVvgs3_k&s=G7SkWT-fSV9d3SJmpWEUUGp2a6mGotbG-uAwNZftpIo&e=">https://doi.org/10.7554/eLife.45077</a> . For additional data or questions, please contact author Aby Joseph (aby.joseph@rochester.edu, dreamworks1991@gmail.com)</p>
Landscape and habitat data for Tetramorium ant species from Cordonnier et al. 2019 Landscape Ecology
<p>This README accompanies the file "data_Cordonnier_LandEcol.txt"</p> <p>Associated publication : </p> <p>Multi-scale impacts of urbanization on species distribution within the genus <br> <em>Tetramorium </em>- Landscape Ecology<br> M. Cordonnier, C. Gibert, A. Bellec, B. Kaufmann, G. Escarguel</p> <p> <br> ********************************** CONTENTS ***********************************<br> The data are in table form with TABs as variables field delimiters so they can <br> be readily imported in any statistical package or spreadsheet program. Please, <br> contact me if you need the file formatted otherwise. </p> <p>This file includes a description of the variables.</p> <p>The individuals described in this file were identified to species and analyzed for climate variables in</p> <p>Cordonnier, M., Bellec, A., Dumet, A., Escarguel, G., & Kaufmann, B. (2019). <br> Range limits in sympatric cryptic species: a case study in Tetramorium pavement <br> ants (Hymenoptera: Formicidae) across a biogeographical boundary. Insect <br> Conservation and Diversity, 12(2), 109-120.<br> </p> <p>*******************************************************************************<br> Variable names and descriptions</p> <p>ID Sample name<br> X Longitude in WGS 84 (World Geodetic System 1984) decimal degrees rounded to 5 decimal places<br> Y Latitude in WGS 84 (World Geodetic System 1984) decimal degrees rounded to 5 decimal places<br> SZ Name of the sampling area sensu Cordonnier et al. (2019)<br> SP Species identification based on mtDNA COI gene<br> PI10 Percentage of impervious surfaces within a 10 m buffer around the sample<br> PI30 Percentage of impervious surfaces within a 30 m buffer around the sample<br> PI500 Percentage of impervious surfaces within a 500 m buffer around the sample<br> MH1 Presence / absence of full soil with vegetation <br> MH2 Presence / absence of pavement <br> MH3 Presence / absence of unstabilized material (sand. gravel. compacted soil <br> with pebbles or small rocks) <br> MH4 Presence / absence of wood or root <br> MH5 Presence / absence of litter (woodchips or dead leaves) <br> MH6 Presence / absence of curb <br> MH7 Presence / absence of building <br> MH8 Presence / absence of feature (p.ex. lamp post. elec. pole. large rock) <br> MH9 Presence / absence of ditch or strong slope</p> <p>********************************* CONTACT **********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com</p> <p>*******************************************************************************<br> </p>
Vortex input files - Carroll et al. Scientific Reports
<p>Vortex input files associated with the manuscript by Carroll et al. "Biological and sociopolitical sources of uncertainty in population viability analysis for endangered species recovery planning", published in Scientific Reports http://doi.org/10.1038/s41598-019-45032-2</p> <p>v2013nodd.xml: Scenario without density-dependent reproduction adapted from 2013 Mexican wolf PVA, as presented initially in Carroll et al. 2014. https://doi.org/10.1111/cobi.12156<br> v2013dd.xml: Scenario with density-dependent reproduction adapted from 2013 Mexican wolf PVA.<br> v2017.xml: Scenario adapted from 2017 Mexican wolf PVA (Miller 2017). <br> MXWlivNoMex.txt: Pedigree input file for above scenarios.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Simulated dataset (Almeida et al., 2018 - GigaScience) treated with various clustering programs to evaluate ReClustOR efficiency and constitency
<p>ReClustOR is a novel clustering method that overcomes some of the problems associated with classical ‘<em>heuristic’</em>clustering methods and consequently increases the stability and quality of the reconstructed OTUs. Moreover, the OTUs database defined with ReClustOR can be used as reference(s) with gradual enrichment of it, with new studies and samples. In this way, huge datasets like<em> </em>the Earth Microbiome Project can be easily used as references for smaller projects, thereby increasing the quality of comparisons between studies and datasets</p> <p>Here, we propose a new approach called ReClustOR (for RE-CLUSTering method using an Open-Reference approach) to improve OTU consistency (see https://doi.org/10.5281/zenodo.2597402). This new strategy combines two of the previously-described clustering methods. Firstly, a classical clustering<em> </em>method (<em>e.g. </em>SWARM, or VSEARCH) is used to define OTU centroids and create a reference database. Secondly, a closed- or open-reference method (depending on the user’s choice) is computed for all reads which are not considered as OTU centroids. Contrary to the classical clustering methods, each read is compared to all centroids using a distance-based greedy clustering technique (Edgar, 2010; He et al., 2015), and then assigned to the nearest one, thereby fixing the erroneous assignments of reads to OTUs.</p> <p>To highlight the improvements provided by ReClustOR in describing microbial diversity in terms of ecological diversity metrics (<em>e.g. </em>richness, OTU composition, Shannon, 1/Simpson) and taxonomic composition, a simulated dataset was subjected to: (i) ESV definition, (ii) multiple conventional <em>de novo</em> methods (<em>i.e.</em> a homemade <em>de novo </em>clustering close to CRUNCHCLUST, VSEARCH and SWARM), and (iii) ReClustOR computation. This dataset is a simulated one (Almeida et al., 2018), containing a diverse set of <em>genera</em> commonly found in three ecosystems different ecosystems: human gut, ocean and soil. The clustering methods were compared for: (i) their ability to describe microbial richness, (ii) the congruence between OTU assignments and sequences taxonomy, (iii) the robustness of each defined OTU, and (iv) their ability to efficiently describe the microbial community based on OTU composition.</p> <p>Here, the simulated dataset (00_Raw_data) and all steps of analysis are available to resue them to test ReClustOR, and also to have a better understanding of files and data produced by this program. More details are available in the Tree_of_data.tree file.</p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
Data from: Paule et al. (2020) Phylogenomic insights into the Fascicularia-Ochagavia group (Bromelioideae, Bromeliaceae). Botanical Journal of the Linnean Society (doi: 10.1093/botlinnean/boz085)
<p>DNA sequence alignments as well as the input and output files which specify the different data partitioning schemes used for phylogenetic analyses in Paule et al. (2020) Phylogenomic insights into the Fascicularia-Ochagavia group (Bromelioideae, Bromeliaceae). Botanical Journal of the Linnean Society (doi: 10.1093/botlinnean/boz085).</p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
CLDF dataset derived from Liú et al.'s "Collection of Basic Words in Chinese Dialects" from 2007
<p>Cite the source of the dataset as:</p> <blockquote> <p>Líu, L.; Wáng, H.; Bǎi, Y. (2007): Xiàndài Hànyǔ fāngyán héxīncí, tèzhēng cíjí 现代汉语方言核心词·特征词集 [Collection of basic vocabulary words and characteristic dialect words in modern Chinese dialects]. Nánjīng: Fènghuáng.</p> </blockquote>
CLDF dataset derived from Galucio et al.'s "Lexical Distances within the Tupian Linguistic family" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Galucio, Ana Vilacy and Meira, Sérgio and Birchall, Joshua and Moore, Denny and Gabas Júnior, Nilson and Drude, Sebastian and Storto, Luciana and Picanço, Gessiane and Rodrigues, Carmen Reis. (2015). Genealogical relations and lexical distances within the Tupian linguistic family. Boletim do Museu Paraense Emílio Goeldi. Ciências Humanas, 10(2), 229-274. https://dx.doi.org/10.1590/1981-81222015000200004</p> </blockquote>
CLDF dataset derived from Dellert et al.'s "NorthEuraLex (Version 0.9)" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Dellert, J., Daneyko, T., Münch, A. et al (2020). NorthEuraLex (Version 0.9). Lang Resources and Evaluation. https://doi.org/10.1007/s10579-019-09480-6</p> </blockquote>
CLDF dataset derived from Nagaraja et al.'s "A Lexicostatistical Study of the Khasian Languages" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Nagaraja KS, Sidwell P & Greenhill SJ. 2013. A Lexicostatistical Study of the Khasian Languages: Khasi, Pnar, Lyngngam, and War. Mon-Khmer Studies Journal, 42, 1-11.</p> </blockquote>
Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)
<p>This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br>The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase.<br>For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder.<br>For further measurement details, please refer to the mentioned original article.</p>
CLDF dataset derived from Heath et al's "Dogon Comparative Wordlist" from 2016
<p>Cite the source of the dataset as:</p> <blockquote> <p>Moran, Steven & Forkel, Robert & Heath, Jeffrey (eds.) 2016. Dogon and Bangime Linguistics. Jena: Max Planck Institute for the Science of Human History. (Available online at http://dogonlanguages.info, Accessed on 2024-08-12.)</p> </blockquote>
CLDF dataset derived from Greenhill et al.'s "Austronesian Basic Vocabulary Database" from 2020 focusing on Philippine languages
<p>Cite the source of the dataset as:</p> <blockquote> <p>Greenhill, S.J., Blust. R, & Gray, R.D. (2008). The Austronesian Basic Vocabulary Database: From Bioinformatics to Lexomics. Evolutionary Bioinformatics, 4:271-283.</p> </blockquote>
Ramirez, et al, 2008: Ramirez et al, 2008
<p>Ramirez, L., Diniz-Filho, J. A. F. and Hawkins, B. A. (2008), Partitioning phylogenetic and adaptive components of the geographical body-size pattern of New World birds. Global Ecology and Biogeography, 17: 100–110. doi: 10.1111/j.1466-8238.2007.00346.x</p>
Ramirez, et al, 2008: Ramirez et al, 2008
Ramirez, L., Diniz-Filho, J. A. F. and Hawkins, B. A. (2008), Partitioning phylogenetic and adaptive components of the geographical body-size pattern of New World birds. Global Ecology and Biogeography, 17: 100–110. doi: 10.1111/j.1466-8238.2007.00346.x<p></p>Ramirez, L., Diniz-Filho, J. A. F. and Hawkins, B. A. (2008), Partitioning phylogenetic and adaptive components of the geographical body-size pattern of New World birds. Global Ecology and Biogeography, 17: 100–110. doi: 10.1111/j.1466-8238.2007.00346.x
Price SA et al, 2012: Price et al, 2012
Price SA, Hopkins SSB, Roth VL, Smith KK(2012) Data from: Tempo of trophic evolution and its impact on mammalian diversification. Dryad Digital Repository. <p></p>http://dx.doi.org/10.5061/dryad.vr28vf67<p></p>Price SA, Hopkins SSB, Roth VL, Smith KK(2012) Data from: Tempo of trophic evolution and its impact on mammalian diversification. Dryad Digital Repository. <p></p>http://dx.doi.org/10.5061/dryad.vr28vf67
Price SA et al, 2012: Price SA, et al 2012
Price SA, Hopkins SSB, Roth VL, Smith KK(2012) Data from: Tempo of trophic evolution and its impact on mammalian diversification. Dryad Digital Repository. <p></p>http://dx.doi.org/10.5061/dryad.vr28vf67<p></p>A database of diets of mammalian species was constructed from published accounts drawn from primary research with data obtained through analysis of stomach or cheek pouch contents or the contents of food stores, direct behavioral observation, or fecal analysis.
Leblanc et al, 2012
<p>Leblanc, Karine; Arístegui Ruiz, Javier; Armand, L; Assmy, P; Beker, B; Bode, Antonio; Breton, E; Cornet, V; Gibson, J; Gosselin, M-P; Kopczynska, EE; Marshall, Harold G; Peloquin, Jill M; Piontkovski, S; Poulton, Alex J; Quéguiner, Bernard; Schiebel, Ralf; Shipe, R; Stefels, Jacqueline; van Leeuwe, Maria A; Varela, M; Widdicombe, Claire E; Yallop, M (2012): A global diatom database - abundance, biovolume and biomass in the world ocean. Earth System Science Data, 4, 149-165, doi:10.5194/essd-4-149-2012.</p> <p> </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.