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Fig. 6 in To The Knowledge Of Long-Horned Beetles (Coleoptera: Cerambycidae) Of The Oriental Region. Part 3.
Fig. 6. Epepeotes nitidus (Aurivillius, 1924)
Fig. 11 in To The Knowledge Of Long-Horned Beetles (Coleoptera: Cerambycidae) Of The Oriental Region. Part 3.
Fig. 11. Pelargoderus vittatus Audinet-Serville, 1835.
Fig. 10 in To The Knowledge Of Long-Horned Beetles (Coleoptera: Cerambycidae) Of The Oriental Region. Part 2.
Fig. 10. Pterolophia zebrina zebrina (Pascoe, 1858)
Fig. 14. Parabryna polysana Vives, 2015 in To The Knowledge Of Long-Horned Beetles (Coleoptera: Cerambycidae) Of The Oriental Region. Part 1
Fig. 14. Parabryna polysana Vives, 2015
Fig. 13 in To The Knowledge Of Long-Horned Beetles (Coleoptera: Cerambycidae) Of The Oriental Region. Part 1
Fig. 13. Dystasia semicana (Pascoe, 1864)
Hydrologic data from long-term research catchments at the Santa Rita and Jornada Experimental Ranges
<p>This datasets contains ecohydrological data collected for the research catchments at the Santa Rita Experimental Range (SRER, 31.817° N, -110.851° W) in southern of Arizona and the Jornada Experimental Range (JER, 32.585° N, -106.603° W) in southern New Mexico. The SRER catchment correspond to a mesquite savanna of the Sonoran Desert and the JER catchment is a mixed shrubland of the Chihuahuan Desert.</p> <p>Dataset includes the following ecohydrological variables at a daily scale:</p> <ul> <li>Year of measurement.</li> <li>Day of year (DOY) of measurement.</li> <li>Precipitation (<em>P</em>).</li> <li>Evapotranspiration (<em>ET</em>).</li> <li>Runoff (<em>Q</em>).</li> <li>Soil moisture (<em>SM</em>).</li> <li>Net Radiation (<em>R<sub>n</sub></em>).</li> <li>Sensible heat flux (<em>H</em>).</li> <li>Latent heat flux (<em>λET</em>).</li> <li>Gross primary production (<em>GPP</em>).</li> <li>Ecosystem respiration (<em>R<sub>eco</sub></em>).</li> <li>Net ecosystem exchange (<em>NEE</em>).</li> <li>Air Temperature (<em>Tair</em>).</li> <li>Relative Humidity (<em>RH</em>).</li> <li>Vapour Pressure Deficit (<em>VPD</em>).</li> </ul> <p>Please cite the next data note together with the dataset:</p> <p>Vivoni, E. R., Perez-Ruiz, E. R., Keller, Z. T., Escoto, E. A., Templeton, R. C., Templeton, N. P., Anderson, C. A., Schreiner-McGraw, A. P., Mendez-Barroso, L. A., Robles-Morua, A., Scott, R. L., Archer, S. R., Peters, D. P. C. 2021. Long-term Research Catchments to Investigate Shrub Encroachment in the Sonoran and Chihuahuan Deserts: Santa Rita and Jornada Experimental Ranges. <em>Hydrological Processes</em>. 35: e14031. <a href="https://urldefense.com/v3/__http://doi.org/10.1002/hyp.14031__;!!IKRxdwAv5BmarQ!OTbdNyLUWzTumWFs0u8j8dW3TT9O3UzM6XrcR-ReiWzf7vxHHKdNTKy8wR27fiHR$">http://doi.org/10.1002/hyp.14031</a></p> <p>This new version incudes three new variables (<em>Tair</em>, <em>RH</em> and <em>VPD</em>), and some carbon and water fluxes were updated to match data used in publications 1 and 2.</p> <p>Related publications:</p> <ol> <li>Perez-Ruiz, E.R., Vivoni, E.R., and Sala, O.E. 2022. <a href="https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4189" target="_blank" rel="noreferrer noopener">Seasonal Carryover of Water and Effects on Carbon Dynamics in a Dryland Ecosystem</a>. <em>Ecosphere.</em> 13(7): e4189.</li> <li>Vivoni, E.R., Perez-Ruiz, E.R., Scott, R.L., Naito, A.T., Archer, S.R., Biederman, J.A., and Templeton, N.P. 2022. <a href="http://www.sciencedirect.com/science/article/pii/S0168192321004494" target="_blank" rel="noreferrer noopener">A Micrometeorological Flux Perspective on Brush Management in a Shrub-encroached Sonoran Desert Grassland</a>. <em>Agricultural and Forest Meteorology</em>. 313: 108763.</li> <li>Schreiner-McGraw, A.P., and Vivoni, E.R. 2018. <a href="http://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018WR022842">On the Sensitivity of Hillslope Runoff and Channel Transmission Losses in Arid Piedmont Slopes.</a> <em>Water Resources Research</em>. 54(7): 4498-4518.</li> <li>Schreiner-McGraw, A.P., and Vivoni, E.R. 2017. <a href="http://onlinelibrary.wiley.com/doi/10.1002/ecs2.2000/full">Percolation Observations in an Arid Piedmont Watershed and Linkages to Historical Conditions in the Chihuahuan Desert.</a> <em>Ecosphere</em>. 8(11): e02000, <a href="http://dx.doi.org/10.1002/ecs2.2000">http://dx.doi.org/10.1002/ecs2.2000</a></li> <li>Anderson, C.A., and Vivoni, E.R. 2016. <a href="http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015WR018016/abstract">Impact of Land Surface States within the Flux Footprint on Daytime Land-Atmosphere Coupling in Two Semiarid Ecosystems of the Southwestern U.S.</a> <em>Water Resources Research.</em> 52: 4785-4800.</li> <li>Schreiner-McGraw, A.P., Vivoni, E.R., Mascaro, G., and Franz, T.E. 2016. <a href="http://www.hydrol-earth-syst-sci.net/20/329/2016/hess-20-329-2016.html">Closing the Water Balance with Cosmic-ray Soil Moisture Measurements and Assessing Their Relation with Evapotranspiration in Two Semiarid Watersheds.</a> <em>Hydrology and Earth System Sciences</em>. 20: 329-345.</li> <li>Pierini, N.A., Vivoni, E.R., Robles-Morua, A., Scott, R.L., and Nearing, M.A. 2014. <a href="http://onlinelibrary.wiley.com/doi/10.1002/2014WR015781/abstract">Using Observations and a Distributed Hydrologic Model to Explore Runoff Threshold Processes Linked with Mesquite Encroachment in the Sonoran Desert.</a> <em>Water Resources Research</em>. 50(10): 8191–8215.</li> <li>Templeton, R.C., Vivoni, E.R., Méndez-Barroso, L.A., Pierini, N.A., Anderson, C.A., Rango, A., Laliberte, A.S., and Scott, R.L. 2014. <a href="http://www.sciencedirect.com/science/article/pii/S0022169413008718">High-Resolution Characterization of a Semiarid Watershed: Implications on Evapotranspiration Estimates.</a> <em>Journal of Hydrology</em>. 509: 306-319.</li> <li>Vivoni, E.R., Rango, A., Anderson, C.A., Pierini, N.A., Schreiner-McGraw, A., Saripalli, S., and Laliberte, A.S. 2014. <a href="http://www.esajournals.org/doi/abs/10.1890/ES14-00217.1">Ecohydrology with Unmanned Aerial Vehicles.</a> <em>Ecosphere</em>. 5(10): art130. <a href="http://dx.doi.org/10.1890/ES14-00217.1">http://dx.doi.org/10.1890/ES14-00217.1</a></li> </ol> <p> </p>
Assessment of the Effectiveness of Coarse Resolution Fire Products in Monitoring Long-term Changes in Fire Regime within Protected Areas in South Africa
<p>The dataset includes fire histrory (shapefiles with data perimeters for 2003-2020) within 3 protected areas in South Africa, Blyde, Manyeleti, and Songimvelo.</p>
Observed changes in significant wave heights derived from long-term homogenized measurements offshore mainland Portugal
<p>The data set contains statistical estimators of significant height acquired over the last 40 years by wave stations operating on the Portuguese coast; Monthly Mean, Monthly Maximum and the monthly 99, 95, 90, 50 , 10 percentiles</p> <p>The data was calculated based on long-term homogenized 3h series of in-situ significant wave height</p> <p>These buoys have been maintained by the Instituto Hidrografico and constitute one of the longest time series of wave data.</p> <p>Each file correspond to a particular wave station. All files are in ASCII and have the same 9 column format. The heading identify the variables</p> <p>Leixoes_corr_monthly_percentiles.txt - Data acquired by Leixoes buoy: Location 41º 19.00' N 8º 59.00' W, depth app 100 m</p> <p>Sines_corr_monthly_percentiles.txt - Data acquired by Sines buoy: Location 37º 55.27' N 8º 55.73 ' W, depth app 100 m</p> <p>Faro_coor_monthly_percentiles.txt - Data acquired by Faro buoy: Location 36º 54.28' N 7º 53.90' W, depth app 100 m</p> <p> </p>
Data for Huang, Luo, et al., Dopamine-mediated interactions between short- and long-term memory dynamics, Nature, 2024
<p>Zip files contain voltage imaging traces and on-ball behavioral data from the five main figures in Huang, Luo, et al., Dopamine-mediated interactions between short- and long-term memory dynamics, Nature, 2024</p>
Raw data to "Quantum melting of long-range ordered quantum antiferromagnets investigated by momentum-space continuous similarity transformations"
<p>This collection of data is complementary to the publication "Quantum melting of long-range ordered quantum antiferromagnets investigated by momentum-space continuous similarity transformations", Dag-Björn Hering, Matthias R. Walther, Kai P. Schmidt, Götz S. Uhrig, arXiv:2405.13768 (https://arxiv.org/abs/2405.13768).</p> <p>It contains three data sets:</p> <ul> <li>raw_data: Contains the results and metadata of individual CST runs in a ".json" format</li> <li>processed_data: Contains processed raw data in a “.pkl” format, where derived quantities are directly accessible (sublattice magnetization, second derivative of the ground-state energy) </li> <li>plot_data: Contains the data points used in Figs 4,6,7,8,910,11,12 and 13 in a “.csv” format</li> </ul> <p>For details on the CST, the used error estimates and physical quantities we refer the the publication.</p> <p>For details on the format, we recommend the README.md files.</p>
Data and Codes for "Long-Range Repulsion Between Chromosomes in Mammalian Oocyte Spindles"
<p>Complete source data and analysis codes for the paper "Long-Range Repulsion Between Chromosomes in Mammalian Oocyte Spindles" by Colm P Kelleher, Yash P Rana, and Daniel J Needleman</p> <p> </p> <p>Updates available at https://osf.io/p9t7z/</p>
The Maintenance of Long-Lived Concentric Eyewall in Simulated Typhoon Lekima (2019)
<p>The model-simulated data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (qnn_nancy@yahoo.com).</p>
TOKIO Year-long Dataset
<p>This dataset contains the Darshan logs from which the feature vectors generated for "A Year in the Life of a Parallel File System" by Lockwood et al, presented at the 2018 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'18), were derived. The Darshan logs are stored in the binary format dictated by Darshan 3.1 as generated on the Cori, Edison, and Mira systems.</p> <p>This dataset also includes the feature vectors used in the aforementioned paper, encoded in CSV format. These feature vectors are identical to those also included in the tokio-abcutils code repository that contains all analysis and figures presented. They are simply duplicated here for convenience.</p> <p>This tar file contains the following:</p> <ul> <li>README.md - This file</li> <li>LICENSE.md - The license under which these data have been published</li> <li>INDEX.csv - An index of all Darshan logs included in this dataset. It contains <ol> <li><code>log_file</code>: the name of each Darshan log</li> <li><code>date</code>: The date on which the job was run and the log was generated</li> <li><code>compute_system</code>: Whether the job was run on Edison, Cori, or Mira</li> <li><code>file_system</code>: The file system on which the job was run. Required to disambiguate jobs run on Edison, which has three file systems.</li> <li><code>application</code>: The name of the benchmark application used to generate the data. IOR and HACC benchmarks are named accordingly; <code>dbscan_read</code> is BD-CATS and <code>vpicio_uni</code> is VPIC.</li> <li><code>shared_or_fpp</code>: Whether the job generated file-per-process or single-shared-file I/O. Required to disambiguate IOR jobs which were run in both modes.</li> <li><code>read_or_write</code>: Whether the job predominantly performed reads or writes. Requires to disambiguate IOR jobs which were run in both modes.</li> <li><code>md5</code>: The MD5 digest for the Darshan log.</li> </ol> </li> <li>darshan_logs/ - Directory containing actual Darshan logs. Logs are sorted into subdirectories according to the year and month in which they were generated.</li> <li>summaries/ - Directory containing the feature vector CSVs that fed into the plots presented in the paper. Identical to the eponymous files in the <a href="https://dx.doi.org/10.5281/zenodo.1345786">tokio-abcutils release</a>'s <code>summaries</code> subdirectory.</li> <li>index_darshan_logs.py - The script used to generate <code>INDEX.csv</code> from the individual Darshan logs. Useful to determine exactly how the columns in INDEX.csv map to the raw data in each log.</li> <li>organize_logs_by_date.py - The script used to collect the Darshan logs into date-indexed subdirectories. Provided for provenance.</li> </ul>
Analysis results for association study of long-term kidney transplant rejection using whole-exome sequencing
<p>Association study results for long-term kidney transplant rejection. Single-variant association results are provided as Plink output files. Meta-analysis results are provided as METAL output files. FDR results are sorted lists of the top association result from random sample label permutations and are included with the plink and meta-analysis results. SKAT and GSEA results are provided for gene and pathway level analyses, respectively.</p>
Testing dataset for the "long-bone-diaphyseal-CSG-Toolkit"
<p>The present dataset has been used for the validation study of correct operation for the "long-bone-diaphyseal-CSG-Toolkit". It consists of three 3D mesh bone models <em>(a humerus, a femur and a tibia, which are part of the Athens modern reference skeletal collection)</em> used for comparison to alternative methods for calculating CSG properties of long bones and one 3D mesh ground model <em>(with known geometric properties)</em> used as a gold standard reference.</p> <p>Additionally, the dataset includes all the results <em>(stored in the respective csv files)</em> from analyzing each of these models with the GNU Octave CSG Toolkit v1.0.1. The present dataset acts both as supplementary material to the validation study and as a sample dataset for user testing of the operation of the GNU Octave CSG Toolkit.</p>
SPAligner: alignment of long error-prone reads to assembly graphs
<p>This repository contains benchmarking datasets and scripts for the manuscript "SPAligner: alignment of long error-prone reads to assembly graphs". </p> <p>Graph representation of genome assemblies has been recently used in different applications — from gene finding to haplotype separation. While many of these applications are based on aligning DNA and protein sequences to assembly graphs, existing software tools for finding such alignments have important limitations. We present a novel SPAligner (Saint Petersburg Aligner) tool for aligning long reads to assembly graphs and demonstrate that it generates accurate alignments.</p> <p> </p> <p> </p>
Drier tropical forests are susceptible to functional changes in response to a long-term drought
<p>Maps created and resulting data from analysis in changes in community weighted mean of traits. The raw trait data and forest census data used are available from their sources in www.<a href="http://gem.tropicalforests.ox.ac.uk/">gem.tropicalforests.ox.ac.uk</a> and ForestPlots.net.</p>
Fast and Luminous Transients from the Explosions of Long-lived Massive White Dwarf Merger Remnants
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017ApJ...850..127B">Brooks et al. (2017)</a>. MESA version 9793.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aa9568">10.3847/1538-4357/aa9568</a></p>
Long term bearded pig camera trap data across the SAFE landscape.
<p><strong>Description: </strong></p> <p>Data on camera trap surveys and capture events for bearded pigs across the SAFE landscape from 2011-2017.<br> Data was collected by Dr Oliver Wearn from 2011 to 2014, by Phil Chapman from 2015 to 2016 and by Charlie Davison in 2017.<br> Used to assess how bearded pigs are responding to land-use change in Sabah. NB: These data are a subset of the full SAFE Project core mammal trapping data, but include additional details about bearded pig social structure and abundances.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/173"><strong>Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=312">here</a></p> <p><strong>Files: </strong>This consists of 1 file: DavisonBeardedPigs.xlsx</p> <p><strong>DavisonBeardedPigs.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>Deployments</strong> (described in worksheet Deployments)</p> <p>Description: Data relating to all random camera trap deployments</p> <p>Number of fields: 5</p> <p>Number of data rows: 833</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement point on SAFE project core grids (Field type: Location)</li> <li><strong>Date.On</strong>: Date survey started (Field type: Date)</li> <li><strong>Date.Off</strong>: Date survey ended (Field type: Date)</li> <li><strong>CTNs</strong>: Length of survey (camera trap nights). Zero if camera was faulty. (Field type: Numeric)</li> <li><strong>Landuse</strong>: Land-use type (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Records</strong> (described in worksheet Records)</p> <p>Description: Data relating to all camera trap records of bearded pigs across all land-uses, and humans and domestic dogs in Oil palm; data generated from individual camera trap images</p> <p>Number of fields: 13</p> <p>Number of data rows: 4236</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement location on SAFE project core grids (Field type: Location)</li> <li><strong>Date</strong>: Date of photo capture (Field type: Date)</li> <li><strong>Time</strong>: Time of photo capture (Field type: Time)</li> <li><strong>Ambient.Temp</strong>: Temperature at the time of photo capture (Field type: Numeric)</li> <li><strong>Moon.Phase</strong>: Moonphase at time of photo capture (Field type: Categorical)</li> <li><strong>Species</strong>: Identity of the individual(s) (Field type: Taxa)</li> <li><strong>Soc.Str</strong>: Social structure (Field type: Categorical)</li> <li><strong>Sex</strong>: Sex of the individual (Field type: Categorical)</li> <li><strong>No.Individuals</strong>: Number of individuals in survey (Field type: Abundance)</li> <li><strong>No.Juveniles</strong>: Number of adults in survey (Field type: Abundance)</li> <li><strong>No.Subadults</strong>: Number of subadults in survey (Field type: Abundance)</li> <li><strong>No.Adults</strong>: Number of juveniles in survey (Field type: Abundance)</li> <li><strong>Land-use</strong>: Land use type (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-04-30 to 2018-04-01</p> <p><strong>Latitudinal extent: </strong>4.6350 to 4.7538</p> <p><strong>Longitudinal extent: </strong>116.9472 to 117.6253</p> <p><strong>Taxonomic coverage: </strong><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <p>Animalia<br>  - Chordata<br>  -  - Mammalia<br>  -  -  - Artiodactyla<br>  -  -  -  - Suidae<br>  -  -  -  -  - <em>Sus</em><br>  -  -  -  -  -  - <em>Sus barbatus</em><br>  -  -  - Carnivora<br>  -  -  -  - Canidae<br>  -  -  -  -  - <em>Canis</em><br>  -  -  -  -  -  - <em>Canis lupus</em><br>  -  -  -  -  -  -  - <em>Canis lupus familiaris</em><br>  -  -  - Primates<br>  -  -  -  - Hominidae<br>  -  -  -  -  - <em>Homo</em><br>  -  -  -  -  -  - <em>Homo sapiens</em></p> <p> </p>
Data from: Heritability of growth and leaf loss compensation in a long-lived tropical understorey palm
<p>Data from: Heritability of growth and leaf loss compensation in a long-lived tropical understorey palm, PLOS ONE.</p> <p>Quantitative genetic data related to growth parameters and leaf loss compensation of 1402 <em>Chamaedorea elegans</em> seedlings. Data was collected in a greenhouse experiment performed at the Unifarm experimental facilities of Wageningen University between January 2013 and July 2014, in which a defoliation treatment was applied. Seeds were collected in the Montes Azules Biosphere Reserve in Chiapas, Mexico. See PLOS ONE publication for more details.</p> <p>The file contains a separate "read me" tab in which the meaning of the variable codes is explained. </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.