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X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
<p><span></span></p> <p><span>Annual rings from vines in a 30 year old, California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were measured at the beginning and end of the lifetime of the vineyard.</span></p> <p><span>X-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. </span></p> <p><span>Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius.</span></p> <p><span>Rootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species.</span></p>
Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity
<p># Data and plotting code for "Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity" by Battulga Munkhbat, Piotr Wróbel, Tomasz J. Antosiewicz, and Timur O. Shegai, ACS Photonics (2022); https://doi.org/10.1021/acsphotonics.2c00433</p> <p><br> ## Contents</p> <p>* <TMD-material>: directories with raw and derived data for all 10 TMDs<br> * f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt: obtained permittivities<br> * plot_*_v1.m: Matlab scripts for plotting data</p> <p>## Description of the data</p> <p>The raw and derived data stored in directories <TMD> contain the following files:</p> <p>* <TMD>/<date>-<TMD>.SEsnap: binary data file with collected data, CompleteEASE format<br> * <TMD>/<date>-<TMD>-E*.mat: ascii text file with permittivity data separated into individual components as exported from CompleteEASE software<br> * <TMD>/<date>-<TMD>-full.mat: ascii text file with fitted model parameters as exported from CompleteEASE software<br> * <TMD>/<TMD>-data/*.txt: selected raw data and fits for all considered samples (Mueller Matrix or Delta/Psi/depolarization).</p> <p>The structure of the data file names is as follows:<br> <order-number-in-CompleteEASE>-s<sample-name>-<data-type>.txt for general ellipsometry (delta, psi, depolarization) or<br> <order-number-in-CompleteEASE>-s<sample-name>-o<in-plane-sample-rotation-number>-mm.txt for Mueller Matrix measurements.</p> <p>The following two scripts can be used to plot the raw measured data (solig lines) along with corresponding fits (black dotted lines):</p> <p>* plot_mm_v1.m: Matlab script for plotting Mueller Matrix data for WTe2 and ReS2<br> * plot_psi_delta_depol_v1.m: Matlab script for plotting psi, delta, and depolarization data for other TMDs</p> <p>The diagonal permittivity tensor data are saved in the f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt files which can be plotted using the plot_permittivity_v1.m Matlab script. The format of this file is as follows:</p> <p>wavelength in nanometers; real part of epsilon_xx; imaginary part of epsilon_xx; real part of epsilon_yy; imaginary part of epsilon_yy; real part of epsilon_zz; imaginary part of epsilon_zz;</p> <p> </p> <p> </p>
The Midlatitude Positive Bay index
<p>The Midlatitude Positive Bay (MPB) index measures the strength of the substorm current wedge. </p> <p> </p> <p>Version 0.2</p> <p>On some occasions, the MPB index shows sudden changes when some/one magnetometer co-rotates into/out of the nightside region. To solve this problem, a filter is designed so that the contributions from magnetometers near the dusk/dawn terminators are weighted. The weights smoothly change from 1.0 to 0.0 as the magnetometers co-rotate from nightside to dayside. The new MPB index is very close to the original MPB index, and the sudden changes disappeared.</p> <p>The updated MPB index and onset are calculated using magnetometers at midlatitudes in the nightside region (the method described in Chu et al. [2015]).</p> <p> </p> <p>Version 0.1</p> <p>The Original MPB index and onset are calculated using magnetometers at midlatitudes in the nightside region (the method described in Chu et al. [2015]).</p> <p>The MPB index starts from 1991-01-01/00:00:00 and ends on 2019-12-31/23:59:00. Therefore, it has a 1-min resolution and has a total of 15252480.</p> <p>The unit of the MPB index is nT^2 instead of nT. So, you may want to use sqrt(MPB) instead of the raw MPB. </p> <p>A list of files:</p> <p>The original MPB index is in three formats:</p> <p> mpb_index.mat</p> <p> mpb_index.sav</p> <p> mpb_index.txt.</p> <p>The text file has xMPB, yMPB, and MPB.</p> <p> MPB = xMPB + yMPB.</p> <p>The xMPB and yMPB are the contributions of the H (northward) and D (eastward) components from different stations.</p> <p> </p> <p>The original onset files:</p> <p> onset.mat</p> <p> onset.txt</p> <p>The onset file has substorm onset, peak time, end time, and MPB peak values.</p> <p> </p> <p> </p> <p><br> <br> <br> </p>
Supplemental data for: Variations in the naming of malondialdehyde (MDA) in PubMed-, Scopus-, and Web of Science-indexed literature
<p>Three scientific databases (PubMed, Scopus, and Web of Science (WoS)) were consulted (July 14, 2022) to assess the frequency of eight nomenclatural forms of malondialdehyde (MDA). Due to the peculiarities of the search interface of the selected databases, PubMed was searched in the Title and Abstract fields (search query example in PubMed: "malone dialdehyde"[Title/Abstract]), Scopus was searched in the Title, Abstract, and Keywords fields (search query example in Scopus: TITLE-ABS-KEY (“malone dialdehyde”)), and WoS Core Collection was searched in the Title, Abstract, Author keywords, and Keywords Plus fields (search query example in WoS Core Collection: TS=(“malone dialdehyde”)). All types of publications for the years 2002-2021 are taken into account.</p>
Probabilistic forecasts of the daily maximum of the Kp index produced by the SERENADE prototype model and three empirical models for the period 2010-2018
<p>This dataset contains the probabilistic outputs of SERENADE's first prototype model dedicated to the forecasting of the <span class="math-tex">\(\textit{Kp}_{\textrm{max, 24 h}}\)</span> index for forecasting horizons ranging between 2 and 7 days. The period covered is the one of the SDOML dataset, which is 2010-05 --- 2018-12. All data is contained in a single pickle file, that can be opened in Python, using the following code lines:</p> <p><span class="math-tex">\(\texttt{import pickle}\\ \texttt{with open(DATA_PATH+`/serenade_outputs.pkl', `rb') as f:}\\ ~~~~\texttt{dict_outputs = pickle.load(f)}\)</span></p> <p>The pickle file contains a dictionnary, which itself contains Pandas DataFrames. Each DataFrame corresponds to a forecasting horizon. The dictionnary's keys are the forecasting horizons stored as strings, that is:</p> <p><span class="math-tex">\(\texttt{dict_output.keys() = [`2',`3',`4',`5',`6',`7']}\)</span></p> <p>The DataFrames are indexed by datetime. They contain the observed (true) hourly values of the daily maximum of the Kp index, the forecasts provided by SERENADE and three baseline models (Climatology model, Persistence model and 27-day Recurrence model). The forecast values include the mean and the standard deviation of the forecast normal distributions. Missing moments are due to the absence of EUV images needed to provide the forecast at the given moment.</p> <p> </p>
HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020
<p>The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is stacked by year, and each stack is composed of 12 monthly images in the <strong>NetCDF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”.</p> <p>If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via <a href="mailto:zhangh573@mail2.sysu.edu.cn">zhangh573@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:yqzhao@link.cuhk.edu.hk">zhaoyq66@mail.sysu.edu.cn</a>. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in:</p> <p>Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.</p>
Standardized Dataset of the Ecosystem's Water Use Efficiency, Gross Primary Productivity and the Evapotranspiration Deficit Index for 1982–2017 over the Middle East
<p>This data aimed to investigate the spatial-temporal variability of Standardized Actual Evapotranspiration (sAET), Gross Primary Productivity (sGPP) and Water Use Efficiency (WUE) anomalies series, and the Standardized Evapotranspiration Deficit Index (SEDI). The Middle East (ME), was selected as a case study to monitoring drought events as one of the major natural disasters for the ecosystem. To this end, the yearly gross primary production of GLASS, GIMMS, FloxCom, and VPM datasets for the study area spanning 1982–2017 was used to develop the sGPPR data. On the other hand, the Global Land Evaporation Amsterdam Model (GLEAM-version (v3.3a)), which estimated the several components of terrestrial evaporation (annual actual and potential evaporation (AET, PET)) was used for the same period this aimed to detect the variability of the SEDI.<br> This version of the yearly GLASS-sGPPR dataset (1982–2017) is available for the ME at 0.05° spatial resolution, as the original data of the GPP-GLASS products, While, sGPPR dataset of GIMMS, FloxCom, and VPM are also at annual temporal resolution, and at 0.5 degree spatial resolution spanning 1982–2016 for GIMMS, FloxCom, and 2000-2016 for VPM (Excel wrokbook .xlsx). The SEDI data are also available at 0.25 degree spatial resolution for 1980–2018 ( Raster files (TIFF)). For more details about Standardization of the GPP and evapotranspiration deficit data see: <strong>Alsafadi, K., Al-Ansari, N., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021). An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. <em>Environmental Research Letters</em>. <a href="http://dx.doi.org/10.1088/1748-9326/ac4765">10.1088/1748-9326/ac4765</a></strong></p>
The NLM indexer assignment dataset
<p>This upload contains the NLM indexer assignment dataset which is described in “The NLM indexer assignment dataset: a new large-scale dataset for reviewer assignment research” by Alastair R. Rae, James G. Mork, and Dina Demner-Fushman.</p> <p><br> Contractor_Indexer_Assignments.txt is a pipe delimited file and each line is a record of an article-indexer assignment. Articles are identified by PubMed identifiers (PMIDs) and each indexer is identified by a number. Lines in the file have the following format: "PMID|Indexer Number".</p> <p> </p>
A wildfire risk index and its components and subcomponents - NUTS2 Centro, Portugal
<p>The dataset, presented as a MS Excel file and an ArcGIS Shapefile, includes a wildfire risk index and its components and subcomponents, as well as the results of a clustering process based on the main components of the wildfire risk index. The analysis units are the civil parishes comprised within the NUTS2 Centro territorial unit in central Portugal, identified by name in the column <em>ParishName </em>of the Excel file.</p> <p>All variables are identified in the Excel file Data, and all are included as attributes to the parish polygons in the shapefile.</p>
Text-fig. 4. The studied section showing ichnofabric index and ichnological snapshots on the upper bedding planes of layers No. 1, 12 and 23. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 4. The studied section showing ichnofabric index and ichnological snapshots on the upper bedding planes of layers No. 1, 12 and 23.
Terrestrial Parasite Tracker indexed biotic interactions and review summary
<p>PLEASE CONTACT AUTHORS IF YOU CONTRIBUTED AND WOULD LIKE TO BE LISTED AS A CO-AUTHOR.</p> <p>Terrestrial Parasite Tracker indexed biotic interactions and review summary.</p> <p>The Terrestrial Parasite Tracker (TPT) project began in 2019 and is funded by the National Science foundation to mobilize data from vector and ectoparasite collections to data aggregators (e.g., iDigBio, GBIF) to help build a comprehensive picture of arthropod host-association evolution, distributions, and the ecological interactions of disease vectors which will assist scientists, educators, land managers, and policy makers. Arthropod parasites often are important to human and wildlife health and safety as vectors of pathogens, and it is critical to digitize these specimens so that they, and their biotic interaction data, will be available to help understand and predict the spread of human and wildlife disease.</p> <p>This data publication contains versioned TPT associated datasets and related data products that were tracked, reviewed and indexed by Global Biotic Interactions (GloBI) and associated tools. GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open source software.</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.</p> <p>Funding:<br> The creation of this archive was made possible by the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926</p> <p>References:<br> Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>GloBI Data Review Report</p> <p>Datasets under review:<br> - University of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner. accessed via https://github.com/EMTuckerLabUMMZ/ummzi/archive/6731357a377e9c2748fc931faa2ff3dc0ce3ea7a.zip on 2022-10-12T18:43:37.491Z<br> - Academy of Natural Sciences Entomology Collection for the Parasite Tracker Project accessed via https://github.com/globalbioticinteractions/ansp-para/archive/5e6592ad09ec89ba7958266ad71ec9d5d21d1a44.zip on 2022-10-12T18:45:13.893Z<br> - Bernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology accessed via https://github.com/globalbioticinteractions/bpbm-ent/archive/c085398dddd36f8a1169b9cf57de2a572229341b.zip on 2022-10-12T18:47:33.370Z<br> - Texas A&M University, Biodiversity Teaching and Research Collections accessed via https://github.com/globalbioticinteractions/brtc-para/archive/f0a718145b05ed484c4d88947ff712d5f6395446.zip on 2022-10-12T18:49:42.688Z<br> - Brigham Young University Arthropod Museum accessed via https://github.com/globalbioticinteractions/byu-byuc/archive/4a609ac6a9a03425e2720b6cdebca6438488f029.zip on 2022-10-12T18:50:01.049Z<br> - California Academy of Sciences Entomology accessed via https://github.com/globalbioticinteractions/cas-ent/archive/562aea232ec74ab615f771239451e57b057dc7c0.zip on 2022-10-12T18:50:25.480Z<br> - Clemson University Arthropod Collection accessed via https://github.com/globalbioticinteractions/cu-cuac/archive/6cdcbbaa4f7cec8e1eac705be3a999bc5259e00f.zip on 2022-10-12T18:50:53.662Z<br> - Denver Museum of Nature and Science (DMNS) Parasite specimens (DMNS:Para) accessed via https://github.com/globalbioticinteractions/dmns-para/archive/2a15f657d5e2d7a6ee6359ee30e630bde8fea2ee.zip on 2022-10-12T18:52:36.684Z<br> - Field Museum of Natural History IPT accessed via https://github.com/globalbioticinteractions/fmnh/archive/6bfc1b7e46140e93f5561c4e837826204adb3c2f.zip on 2022-10-12T19:19:24.919Z<br> - Illinois Natural History Survey Insect Collection accessed via https://github.com/globalbioticinteractions/inhs-insects/archive/38692496f590577074c7cecf8ea37f85d0594ae1.zip on 2022-10-12T19:21:30.100Z<br> - UMSP / University of Minnesota / University of Minnesota Insect Collection accessed via https://github.com/globalbioticinteractions/min-umsp/archive/3f1b9d32f947dcb80b9aaab50523e097f0e8776e.zip on 2022-10-12T19:22:18.235Z<br> - Milwaukee Public Museum Biological Collections Data Portal accessed via https://github.com/globalbioticinteractions/mpm/archive/9f44e99c49ec5aba3f8592cfced07c38d3223dcd.zip on 2022-10-12T19:22:42.835Z<br> - Museum for Southwestern Biology (MSB) Parasite Collection accessed via https://github.com/globalbioticinteractions/msb-para/archive/f13bfa0d5493057198639d566f744379c05179f3.zip on 2022-10-12T20:46:06.063Z<br> - The Albert J. Cook Arthropod Research Collection accessed via https://github.com/globalbioticinteractions/msu-msuc/archive/38960906380443bd8108c9e44aeff4590d8d0b50.zip on 2022-10-12T21:02:26.320Z<br> - Ohio State University Acarology Laboratory accessed via https://github.com/globalbioticinteractions/osal-ar/archive/876269d66a6a94175dbb6b9a604897f8032b93dd.zip on 2022-10-12T21:02:46.553Z<br> - Frost Entomological Museum, Pennsylvania State University accessed via https://github.com/globalbioticinteractions/psuc-ento/archive/30b1f96619a6e9f10da18b42fb93ff22cc4f72e2.zip on 2022-10-12T21:02:57.714Z<br> - Purdue Entomological Research Collection accessed via https://github.com/globalbioticinteractions/pu-perc/archive/e0909a7ca0a8df5effccb288ba64b28141e388ba.zip on 2022-10-12T21:03:17.696Z<br> - Texas A&M University Insect Collection accessed via https://github.com/globalbioticinteractions/tamuic-ent/archive/f261a8c192021408da67c39626a4aac56e3bac41.zip on 2022-10-12T21:03:56.509Z<br> - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/4d997dbe8e86398f9f7f4d7851013e788073ae9c.zip on 2022-10-12T21:05:27.222Z<br> - University of Hawaii Insect Museum accessed via https://github.com/globalbioticinteractions/uhim/archive/53fa790309e48f25685e41ded78ce6a51bafde76.zip on 2022-10-12T21:05:40.778Z<br> - University of New Hampshire Collection of Insects and other Arthropods UNHC-UNHC accessed via https://github.com/globalbioticinteractions/unhc/archive/f72575a72edda8a4e6126de79b4681b25593d434.zip on 2022-10-12T21:05:59.319Z<br> - Scott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum. accessed via https://github.com/globalbioticinteractions/unl-nsm/archive/6bcd8aec22e4309b7f4e8be1afe8191d391e73c6.zip on 2022-10-12T21:06:07.054Z<br> - Data were obtained from specimens belonging to the United States National Museum of Natural History (USNM), Smithsonian Institution, Washington DC and digitized by the Walter Reed Biosystematics Unit (WRBU). accessed via https://github.com/globalbioticinteractions/usnmentflea/archive/ce5cb1ed2bbc13ee10062b6f75a158fd465ce9bb.zip on 2022-10-12T21:06:43.102Z<br> - US National Museum of Natural History Ixodes Records accessed via https://github.com/globalbioticinteractions/usnm-ixodes/archive/c5fcd5f34ce412002783544afb628a33db7f47a6.zip on 2022-10-12T21:06:51.935Z<br> - Price Institute of Parasite Research, School of Biological Sciences, University of Utah accessed via https://github.com/globalbioticinteractions/utah-piper/archive/43da8db550b5776c1e3d17803831c696fe9b8285.zip on 2022-10-12T21:07:03.317Z<br> - University of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection accessed via https://github.com/globalbioticinteractions/uwsp-para/archive/f9d0d52cd671731c7f002325e84187979bca4a5b.zip on 2022-10-12T21:07:14.513Z<br> - Giraldo-Calderón, G. I., Emrich, S. J., MacCallum, R. M., Maslen, G., Dialynas, E., Topalis, P., … Lawson, D. (2015). VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic acids research, 43(Database issue), D707–D713. doi:10.1093/nar/gku1117. accessed via https://github.com/globalbioticinteractions/vectorbase/archive/00d6285cd4e9f4edd18cb2778624ab31b34b23b8.zip on 2022-10-12T21:07:22.543Z<br> - WIRC / University of Wisconsin Madison WIS-IH / Wisconsin Insect Research Collection accessed via https://github.com/globalbioticinteractions/wis-ih-wirc/archive/34162b86c0ade4b493471543231ae017cc84816e.zip on 2022-10-12T21:07:52.105Z<br> - Yale University Peabody Museum Collections Data Portal accessed via https://github.com/globalbioticinteractions/yale-peabody/archive/43be869f17749d71d26fc820c8bd931d6149fe8e.zip on 2022-10-12T21:16:57.226Z</p> <p>Generated on:<br> 2022-10-12</p> <p>by:<br> GloBI's Elton 0.12.4 <br> (see https://github.com/globalbioticinteractions/elton).</p> <p>Note that all files ending with .tsv are files formatted <br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv: <br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar: <br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p>datasets.zip:<br> Source datasets used by elton.jar in process of executing the generate_report.sh script.</p> <p>generate_report.sh:<br> Program used to generate the report</p> <p>generate_report.log:<br> Log file generated as part of running the generate_report.sh script</p>
FDTD simulation of 290 nm PAAO with gold nanoparticles: varying refractive index of surrounding medium, s-polarization
<p>Version 2 has the same files as version 1 and some additional files.</p> <p>FDTD software: Lumerical (Ansys, version 2021 R2.3).</p> <p>Structure: aluminum (Palik) substrate; 290 nm thickness (<em>h</em>) aluminum oxide (Palik) layer with 35 nm diameter (<em>RPo</em>) cylindrical pores with 100 nm distance (<em>D</em>) between the pore centers (representing porous anodized aluminum oxide - PAAO); 60 nm diameter (<em>RNP</em>) gold (Johnson and Christy) nanoparticles placed directly above each pore.</p> <p>Refractive index of the surrounding medium (<em>n</em>): 1.0; 1.1; 1.2; 1.3.</p> <p>Simulation region: from 300 nm below the substrate/PAAO interface to 1.3 µm above PAAO surface; x and y spans are equal to one period of the structure.</p> <p>Mesh override region: from 50 nm below the PAAO to 50 nm above the nanoparticles; 2 nm step size in each direction.</p> <p>Light source: BFAST plane wave light source; 500 nm above PAAO; 45° angle of incidence (<em>ang</em>); 300 nm – 1000 nm wavelength range; s-polarization (<em>pol</em>).</p> <p>Monitor (frequency domain field and power): 2D Z-normal; 1 µm above PAAO; results are in "<em>_reflection.txt</em>" files.</p> <p>Information in the file name: <em>h</em> - thickness of PAAO; <em>pol</em> - polarization; <em>RNP/AuRNP</em> - diameter of gold nanoparticles; <em>RPo</em> - diameter of pores; <em>D</em> - distance between pore centers; <em>ang</em> - angle of incidence; <em>n</em> - refractive index of surrounding medium.</p> <p>Files: (1) "<em>_reflection.txt</em>" - lambda(nm) (first column) - wavelength in nanometers; Y (second column) - T data from the monitor above the structure. (2) "<em>_p0.log</em>" - log file produced by the software while running the simulation. (3) "<em>.fsp</em>" - Lumerical software file containing the simulation project; it can be used to extract data from Y- and X-normal monitors (license required to open these files). (4) "<em>Lumerical_Screenshots.pdf</em>" - shows software screenshots for every object and its every property; red text is added to show which values are different for different simulations. (5) "<em>Structure_Illustration.png</em>" - a schematic of modeled structure. (6) "<em>h290,varN.jpg</em>" - a preview of data from "<em>_reflection.txt</em>" files.</p>
PLOS ONE – a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)
<p>This is a dataset used in and produced by research described in article "PLOS ONE - a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)" that is translation of the original Polish text "PLOS ONE – studium przypadku analizy cytowań prac naukowych na podstawie danych otwartego indeksu cytowań (OpenCitations Corpus)" published by EBiB bulletin (2017, No 176).</p> <p>Data were extracted, as nodes (PLOS_cited_nodes.csv) and edges (PLOS_edges.csv) files from the OpenCitations Corpus (http://opencitations.net/download) on 2017.07.25 and describe all cited papers published by PLOS ONE (nodes), and all citing relations (edges). The research was conducted using Gephi (https://gephi.org/) platform so the same source data are also avaiable as GEXF file (for "one-click" import capabilities). In addition, the same data are published in NET format (but be warned that due to this format limitations, information about the publication year of papers has been lost) used by PAJEK platform, as it is very popular tool for analysis of network data.</p> <p>Published figures have prefix names corresponding to figures captions in the original paper, where they have been thoroughly discussed. This data set contains also the additional figure not published in the article, showing most cited paper with citing chains of articles of lenght not greater than 3.<br> These pictures have much better quality than those published in the article, which allows for "drill down"/zoom-in analysis and large format printing.</p>
Table 2: Reliability Indexes of the Measures Used in Grade Three
<p>The procedures of lexical instruction and vocabulary testing continued up to the end of the<br> course when the book content was exhausted. Lastly, near to the end of the academic year, the<br> students took the End of the Course Achievement (ECA) exams (See Appendix 3) the major<br> objectives of which were to make a holistic assessment concerning the achievement of the prespecified<br> course objectives. These tests are usually designed, standardized, and administered either<br> directly by the central offices of the Ministry of Education, or indirectly by the teachers at the local<br> schools. However, no matter who develops them, they follow a uniform scheme or format which is<br> mandated by the officials of the Ministry of Education.<br> The reliability indexes obtained from the batteries utilized in the study are presented in<br> Tables 2</p>
Data and scripts for figures in Walton & Huntingford, "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation", submitted for publication to Environmental Research Letters. Scripts used for plotting and analysis are also included.</p>
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs).
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings).
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs).
Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model
<p>this dataset are “ssociation of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model” Supplementary Material.</p>
Air Quality Index for Indian Cities 2015-2023
<p>This repository contains analysis of AQI Daily bulletins released by the Central Pollution Control Board (CPCB) since 2015. These daily bulletins can be currently obtained here: <a href="https://cpcb.nic.in/AQI_Bulletin.php" rel="nofollow">CPCB Daily AQI Bulletins (link as accessed May 8th, 2024)</a></p> <p>https://github.com/urbanemissions-info/AQI_bulletins contains all the codes for parsing the data from the daily PDFs, data repository, and codes to make the calendar plots.</p> <p>The data contains (available) day-wise <code>city-average AQI value</code>, <code>AQI category</code>, and <code>conditional pollutant</code> information. This data for all cities can be obtained in the <code>AllIndiaBulletins_master.csv</code> from <code>data/Processed</code> folder (on github) or as <code>India_AQI_Bulletins_Master.csv</code> here.</p> <p>Calendar plot of the AQI values is produced for each city, along with the average number of stations reporting AQI value in each year. City wise AQI bulletins CSV and calendar plots can be obtained on UrbanEmissions website: <a href="https://urbanemissions.info/india-air-quality/india-ncap-aqi-indian-cities-2015-2023/" rel="nofollow">Link</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.