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13,064 results for “predictions”
The importance of variation in vital rates and environmental resource availability in predicting demography of a rare understory herb
Plant demography is a function of both the vital rate characteristics of a species (i.e., survival, growth, and reproduction) and the environmental factors that interact with them to create population dynamics. A more detailed understanding of how local-scale environmental factors and variation in individual vital rates shape population-level demographic patterns is needed to improve predictions of population responses to environmental change and implement successful plant conservation strategies. In this study, we examined how individual vital rates for Shortia galacifolia, an endangered, evergreen herb endemic to the southern Blue Ridge Mountains, USA, change as a function of individual size and resource availability and how that variation affects Shortia demography at four sites representing natural and introduced populations using integral projection models (IPMs). We found that Shortia population growth is positively related to individual size and soil moisture. Changes in soil moisture availability altered the importance of survival and growth in predicting Shortia demography but did not affect the contribution of asexual reproduction for most sites. Moreover, changes in vital rate contributions under a low soil moisture scenario were limited to introduced populations growing outside Shortia’s natural climate envelope. Our study underscores the importance of quantifying the influence of individual state characteristics and environmental variables on different vital rates among natural and introduced populations and demonstrates how the combination of these factors can contribute to the success or failure of rare plant populations.
Vertebrate-habitat relationships: Logistic regression models predict probability of occurrence of bird and small mammal species in western Oregon
Logistic regression models predicting probability of occurrence of bird and of small-mammal species were produced using animal-habitat data sets from throughout western Oregon (Garman and Cole 1999 - Vertebrate Habitat Relationships Data Bank (VHRDB), Report to Coastal Landscape Analysis and Modeling Study). Regression coefficients, variables, and metrics related to model predictions are provided here under Entity 1, and in VHRDB as VERTLOGR.
Improving Neutral Density Predictions Using Exospheric Temperatures Calculated on a Geodesic, Polyhedral Grid
<p>These files provide supplemental data, additional graphs of neutral density predictions, and basic program code and required files, to accompany the paper "Improving Neutral Density Predictions Using Exospheric Temperatures Calculated on a Geodesic, Polyhedral Grid,” published in the journal <em>Space Weather, </em>doi:10.1029/2019SW002355. For more information, refer to the READ_ME.pdf documentation and the journal paper.</p>
GTEx v7 prediction models
<p>PrediXcan's prediction models on gene expression from GTEx v7.</p> <p>Also contains LD compilations for S-PrediXcan and S-MultiXcan.</p>
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics
<p>Datasets for manuscript "Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics"</p> <p><strong>Metadata.zip</strong></p> <ol> <li><strong>phenotypes.txt: </strong>tabular file containing binary resistance phenotypes based on CLSI guidelines, where the rows are the isolates and the columns correspond to different drugs. Resistance : 1, susceptibility: 0, missing: intermediate resistant</li> </ol> <p><strong>Features_gpa_exp_snps.zip </strong></p> <p>We provide the processed molecular data as Numpy compressed files (npz.). You can use the Numpy load method to read in these tables https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.htm. The row (strains_list) and column labels (feature_lists) are stored separately.</p> <ol> <li><strong>genexp</strong>: gene expression table directory <ul> <li>genexp_feature_vect.npz: The feature matrix in the numpy format</li> <li>genexp_feature_list.txt: The columns of the feature matrix (features)</li> <li>genexp_strains_list.txt: The rows of the feature matrix (isolates)</li> </ul> </li> <li><strong>gpa: </strong>gene presence/absence table directory <ul> <li>gpa_feature_vect.npz: The feature matrix in the numpy format</li> <li>gpa_feature_list.txt: The columns of the feature matrix (features)</li> <li>gpa_strains_list.txt: The rows of the feature matrix (isolates)</li> </ul> </li> <li><strong>snps: </strong>SNPs table directory <ul> <li>snps_feature_vect.npz: The feature matrix in the numpy format</li> <li>snps_feature_list.txt: The columns of the feature matrix (features)</li> <li>snps_strains_list.txt: The rows of the feature matrix (isolates)</li> </ul> </li> </ol>
Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe): dataset
<p>This is the resulting data set from the prospective observational study (available cases at the time of follow up monitoring) OutCaRe, that is the basis of all results presented in the article "Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe)".</p> <p>OutCaRe is a registered study (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011418).</p>
Paper Repository and References for "Early software defect prediction: A systematic map and review"
<p>Context: Software defect prediction is a trending research topic, and a wide variety of the published papers focus on coding phase or after. A limited number of papers, however, includes the prior (early) phases of the software development lifecycle (SDLC).<br> Objective: The goal of this study is to obtain a general view of the characteristics and usefulness of Early Software Defect Prediction (ESDP) models reported in scientific literature. <br> Method: A systematic mapping and systematic literature review study has been conducted. We searched for the studies reported between 2000 and 2016. We reviewed 52 studies and analyzed the trend and demographics, maturity of state-of-research, in-depth characteristics, success and benefits of ESDP models. <br> Results: We found that categorical models that rely on requirement and design phase metrics, and few continuous models including metrics from requirements phase are very successful. We also found that most studies reported qualitative benefits of using ESDP models.<br> Conclusion: We have highlighted the most preferred prediction methods, metrics, datasets and performance evaluation methods, as well as the addressed SDLC phases. We expect the results will be useful for software teams by guiding them to use early predictors effectively in practice, and for researchers in directing their future efforts.</p>
CNNpred: CNN-based stock market prediction using a diverse set of variables
<p>This dataset contains several daily features of S&P 500, NASDAQ Composite, Dow Jones Industrial Average, RUSSELL 2000, and NYSE Composite from 2010 to 2017. It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of "CNNpred: CNN-based stock market prediction using a diverse set of variables".</p>
Data corresponding to the publication "The limited predictive power of the Pauling Rules"
<p>This is the data set corrresponding to the publication "The limited predictive power of the Pauling rules" (see https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202000829). This data can be reproduced with the following code: https://doi.org/10.5281/zenodo.3654428.</p>
Road traffic prediction dataset.
<p><em>Public (anonymized) road traffic prediction datasets from Huawei Munich Research Center.</em></p> <p>Datasets from a variety of traffic sensors (i.e. induction loops) for traffic prediction. The data is useful for forecasting traffic patterns and adjusting stop-light control parameters, i.e. cycle length, offset and split times.</p> <p>The dataset contains recorded data from 6 crosses in the urban area for the last 56 days, in the form of flow timeseries, depicted the number of vehicles passing every 5 minutes for a whole day (i.e. 12 readings/h, 288 readings/day, 16128 readings / 56 days).</p>
Replication code and data for: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity."
<p>Replication code and data for the paper: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity." The following files are included in this repository:</p> <p>1) R scripts (numbered 0 through 9) include replication code for data analysis</p> <p>2) Datasets (6 zip folders) contain the data analyzed in the R scripts</p> <p> </p> <p> </p>
Stable psychological traits predict perceived stress related to the COVID-19 outbreak
<p>This repository contains the raw dataset associated to the scientific article "Stable psychological traits predict psychological perceived stress to COVID-19 outbreak”, by L. Flesia, V. Fietta, B. Segatto, M. Monaro. Data are contained in the excel file and organized as follows:</p> <p>- the entire dataset used by the authors to perform statistical analysis</p> <p>- the training set used by the authors to train and validate ML models</p> <p>- the test set used by the authors to test the ML models</p> <p>The "Legend" file contains the description of each variable in the excel file.</p> <p>The step by step instructions to replicate the results of ML classification models, which are reported in the paper, including two .arff files containing the training and test set od data that can be directly run in WEKA software 3.9.</p> <p>The "COVID-19 QUESTIONNAIRE" file contains the English version of the questions administered to participants.</p>
Data files associated with the k-nearest neighbor global prediction of isopachs for present to middle Miocene
<p>This compressed dataset (Dataset 1) includes five folders. Each folder contains the observed data, final predictors and final predictions used for global predictions of isopachs from present to mid-Miocene aged sediments. The name of each folder corresponds to the name of the isopach for which the data is related to. For example, the folder named “Isopach0.0_1.8” contains all the data for the 0-1.8-million-year-old isopach.</p> <p> </p> <p>All grids contained within this supplemental material are netCDF4 file format. The grid pitch for all “.nc” files is uniformly at 5-arc minute denoted by “.5m”. Grids are cell-centered sized 4320 x 2160. Within each folder, there contains observed data used in the prediction. This observed data is in centimeters logarithmic base 10 units. Additionally, within each folder there is a final k-NN prediction file and standard deviation (i.e. uncertainty) file. The units for both the prediction file and uncertainty file are in meters.</p> <p> </p> <p>Collectively, there are 85 unique predictor grids. Predictor grid file names adhere to the naming conventions outlined below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension (.nc).</p> <p> </p> <p><strong>Possible interfaces from the top – down:</strong></p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p> </p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), and longitude (and derivatives).</p> <p> </p> <p>Units referenced are as follows:</p> <p> </p> <p>M - meters</p> <p>MS - meters per second</p> <p>DEG – degree</p> <p>DD – decimal degrees</p> <p>M_ASL - meters above sea level (i.e. meters referenced to sea level)</p> <p>TGC_YR-1 - terragram of carbon per year</p> <p>TGYR - terragram per year</p> <p>PSU – percent salinity units</p> <p>MLL – milliliters per liter</p> <p>PCTSAT – percent saturation</p> <p>C – degree centigrade</p> <p>PDW – percent dry weight</p> <p>MCML -micromole per milliliter</p> <p>KG_M-3 – kilogram per cubic meter</p> <p>MG_M-3 – milligram per cubic meter</p> <p>MG_CM-2 - milligram of carbon per square meter</p> <p>MOL_M3-1 – moles per cubic meter</p> <p> </p> <p> </p> <p> </p> <p>Predictor statistics grids are calculated within a given radius (e.g. 10km, 50km, 100km, 125km, 200km, 250km, 500km) of the respective cell-centered value. The statistics grids include mean (.men), the absolute value of the common logarithm (.alg) and the mean of the common logarithm (.mlg). Additionally, some grids are a weighted count (.wct) for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p> </p> <p>Some grids are denoted by “DECADAL_AVERAGE” or “MISSION_MEAN”. These respective markings denote the values within the grid represent the average of values within a decade (10 year) or over the entire mission of the instrument (e.g. reflectance values from satellite mission). Further, an “s” or “x” in addition to the source name indicates additional conditioning on the final grid was required. The “x” simply indicates upsampling or extension to polar regions was performed via various interpolation techniques (e.g. bilinear or machine learning). The “s” indicates there was smoothing (i.e. averaging over set radii) required to produce a qualitatively geologically reasonable global dataset.</p> <p> </p> <p>Appropriate reference naming marker (bold) listed below including example file name (italics), original data source, and date of last access.</p> <p> </p> <p><strong>CRUST1 </strong></p> <p><em>e.g. CM_MANTLE_DEN_KGM3_CRUST1s.5m.nc</em></p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p> </p> <p><strong>GVP</strong></p> <p><em>e.g. GL_VOLCANO_GVP.r10km.wct.5m.nc</em></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p> </p> <p><strong>PLATES</strong></p> <p><em>e.g. GL_DIST_TO_PLATE_BOUNDARY_KM_PLATES.5m.nc</em></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p> </p> <p><strong>ORNL</strong></p> <p><em>e.g. GL_RIVERMOUTH_TSS_TGYR-1_ORNL.5m.nc</em></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), ISLSCP Initiative II Collection. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p> </p> <p><strong>Woa13x</strong></p> <p><em>e.g. SF_AVG_SEA_DENSITY_KGM3_DECADAL_MEAN_woa13x.5m.nc</em></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), NOAA Atlas NESDIS 72, Technical Ed. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p> </p> <p><strong>KIM </strong></p> <p><em>e.g. SF_SEAMOUNTS_KIM.r10km.wct.5m.nc</em></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, Geophysical Journal International, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p> </p> <p><strong>HYCOM</strong></p> <p><em>e.g. SF_CURRENT_EAST_MS_2012_12_HYCOMx.5m.nc</em></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p> </p> <p><strong>NCEDC</strong></p> <p><em>e.g. SF_SHALLOW_QUAKES_NCEDC.r10km.wct.5m.nc</em></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p> </p> <p><strong>Wei2010x</strong></p> <p><em>e.g. SS_BIOMASS_BACTERIA_LOG10_MGCM2_WEI2010x.5m.nc</em></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. PLoS ONE,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p> </p> <p><strong>NGA_egm2008</strong></p> <p><em>e.g. SS_GEOID_M_ABOVE_WGS84_NGA_egm2008.5m.nc</em></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). The EGM2008 Global Gravitational Model, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p> </p> <p><strong>WAVEWATCH3x</strong></p> <p><em>e.g. SS_WAVE_DIRECTION_DEG_2012_12_WAVEWATCH3x.5m.nc</em></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p> </p> <p><strong>Lee</strong></p> <p><em>e.g. SF_TOC_PDW_LEE.5m.nc</em></p> <p>Lee, T. R., Wood, W. T., & Phrampus, B. J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. Global Biogeochemical Cycles, 33(1), 37-46. https://doi.org/10.1029/2018GB005992 Last access: 12/2018.</p> <p> </p> <p><strong>SRTM15+V2</strong></p> <p><em>e.g. GL_ELEVATION_M_ASL_SRTM15+V2.5m.nc</em></p> <p>Tozer, B. , D. T. Sandwell, W. H. F. Smith, C. Olson, J. R. Beale, and P. Wessel, Global bathymetry and topography at 15 arc seconds: SRTM15+, Accepted Earth and Space Science, August 3, 2019. Last Access: 08/2019.</p> <p> </p> <p><strong>GLOBSED_Straume</strong></p> <p><em>e.g. GL_TOT_SED_THICK_M_GLOBSED_Straume.5m.nc</em></p> <p>Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. Geochemistry, Geophysics, Geosystems, 20(4), 1756–1772. https://doi.org/10.1029/2018GC008115. Last Access: 10/2019.</p> <p> </p> <p><strong>Goyetx</strong></p> <p><em>e.g. SS_MIXED_LAYER_DEPTH_MAX_M_Goyetx.5m.nc</em></p> <p>Goyet, C., Healy, R., Ryan, J., and Kozyr, A. Global Distribution of Total Inorganic Carbon and Total Alkalinity below the Deepest Winter Mixed Layer Depths. United States: N. p., 2000. Web. doi:10.2172/760546. Last Access: 10/2013.</p> <p> </p> <p><strong>MODIS_Aqua</strong></p> <p><em>e.g. SS_CHLOROPHYLL_LOG_MG_M3_MODIS_Aqua_MISSION_MEANx.5m.nc</em></p> <p>Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless, D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34( 4), 710– 714. Last Access: 10/2017.</p> <p> </p> <p><strong>SACD_Aquarius</strong></p> <p><em>e.g. SS_WINDSPEED_MS-1_SACD_Aquarius_MISSION_MEANx.5m.nc</em></p> <p>Fore, A. G., Yueh, S. H., Tang, W., Hayashi, A. K., & Lagerloef, G. S. (2013). Aquarius wind speed products: Algorithms and validation. IEEE Transactions on Geoscience and Remote Sensing, 52( 5), 2920– 2927. Last Access: 10/2017.</p> <p> </p> <p> </p>
Within and cross species predictions of plant specialized metabolism genes using transfer learning
<p>Datasets for <em>Within and cross species predictions of plant specialized metabolism genes using transfer learning.</em> </p> <ol> <li>Dataset 1: All gene features used in the full feature machine learning models including expression, co-expression, evolutionary, duplication, and protein domain features.</li> <li>Dataset 2: All gene features used in shared feature machine learning models including expression, evolutionary, duplication, and protein domain features.</li> <li>Table S1: All gene annotations from TomatoCyc or manual annotation.</li> <li>Table S2: All model scores.</li> <li>Table S3: All gene scores and predictions from each model.</li> <li>Table S4: Feature importance for 5 models.</li> <li>Table S5: Statistical analysis between classes for binary and continuous feature data.</li> <li>Table S6: RNAseq datasets used in analysis.</li> </ol>
Supplement to the article "Predicting the morphology of ice particles in deep convection using the super-droplet method" (Shima et al., 2020, GMD)
<p>This is a supplement to the article "Predicting the morphology of ice particles in deep convection using the super-droplet method" authored by Shin-ichiro Shima, Yousuke Sato, Akihiro Hashimoto, and Ryohei Misumi, published in Geosci. Model Dev., 2020.</p> <p>Typical realization of CTRL, simulated by SCALE-SDM 0.2.5-2.2.0</p> <ul> <li>Movie01.QHYD_TYP-CTRL_2.2.0.gif: Spatial structure of the cumulonimbus</li> <li>Movie02.M-D_TYP-CTRL_2.2.0.gif: Mass-dimension relationship of the ice particles</li> <li>Movie03.phi-D_TYP-CTRL_2.2.0.gif : Aspect ratio–dimension relationship of the ice particles</li> <li>Movie04.rho-D_TYP-CTRL_2.2.0.gif : Apparent density–dimension relationship of the ice particles</li> <li>Movie05.V-D_TYP-CTRL_2.2.0.gif : Velocity-dimension relationship of the ice particles</li> </ul> <p>One realization of DX/2, simulated by SCALE-SDM 0.2.5-2.2.0</p> <ul> <li>Movie06.QHYD_DXx0.5_2.2.0.gif: Spatial structure of the cumulonimbus</li> </ul> <p>The same setup as Moves 1-5 (typical realization of CTRL) is used, but simulated by SCALE-SDM 0.2.5-2.2.1</p> <ul> <li>Movie07.QHYD_TYP-CTRL.2.2.1.gif: Spatial structure of the cumulonimbus</li> <li>Movie08.M-D_TYP-CTRL_2.2.1.gif: Mass-dimension relationship of the ice particles</li> <li>Movie09.phi-D_TYP-CTRL_2.2.1.gif: Aspect ratio–dimension relationship of the ice particles</li> <li>Movie10.rho-D_TYP-CTRL_2.2.1.gif: Apparent density–dimension relationship of the ice particles</li> <li>Movie11.V-D_TYP-CTRL_2.2.1.gif: Velocity-dimension relationship of the ice particles</li> </ul> <p>The same setup as Moves 1-5 (typical realization of CTRL) is used, but simulated by SCALE-SDM 0.2.5-2.2.2</p> <ul> <li>Movie12.QHYD_TYP-CTRL.2.2.2.gif: Spatial structure of the cumulonimbus</li> <li>Movie13.M-D_TYP-CTRL_2.2.2.gif: Mass-dimension relationship of the ice particles</li> <li>Movie14.phi-D_TYP-CTRL_2.2.2.gif: Aspect ratio–dimension relationship of the ice particles</li> <li>Movie15.rho-D_TYP-CTRL_2.2.2.gif: Apparent density–dimension relationship of the ice particles</li> <li>Movie16.V-D_TYP-CTRL_2.2.2.gif: Velocity-dimension relationship of the ice particles</li> </ul>
Data from: Genome assembly of the ragweed leaf beetle, a step forward to better predict rapid evolution of a weed biocontrol agent to environmental novelties
<p><span>Rapid evolution of weed biological control agents (BCAs) to new biotic and abiotic conditions is poorly understood and so far, only little considered both in pre-release and post-release studies, despite potential major negative or positive implications for risks of non-targeted attacks or for colonizing yet unsuitable habitats, respectively. Provision of genetic resources, such as assembled and annotated genomes, is essential to assess potential adaptive processes by identifying underlying genetic mechanisms. Here, we provide the first sequenced genome of a phytophagous insect used as a BCA, <i>i.e.</i> the leaf beetle <i>Ophraella communa</i>, a promising BCA of common ragweed, recently and accidentally introduced into Europe. A total 33.98 Gb of raw DNA sequences, representing c. 43-fold coverage, were obtained using the PacBio SMRT-Cell sequencing approach. Among the five different assemblers tested, the SMARTdenovo assembly displaying the best scores was then corrected with Illumina short reads. A final genome of 774 Mb containing 7,003 scaffolds was obtained. The reliability of the final assembly was then assessed by benchmarking universal single-copy orthologous genes (> 96.0% of the 1,658 expected insect genes) and by remapping tests of Illumina short reads (average of 98.6% ± 0.7% without filtering). The number of protein-coding genes of 75,642, representing 82% of the published antennal transcriptome, and the phylogenetic analyses based on 825 orthologous genes placing <i>O. communa </i>in the monophyletic group of Chrysomelidae, confirm the relevance of our genome assembly. Overall, the genome provides a valuable resource for studying potential risks and benefits of this BCA facing environmental novelties.</span></p>
Data from: A rat liver transcriptomic point of departure predicts a prospective liver or non-liver apical point of departure
<p>Identifying a toxicity point of departure (POD) is a required step in human health risk characterization of crop protection molecules, and this POD has historically been derived from apical endpoints across a battery of animal-based toxicology studies. Using rat transcriptome and apical data for 79 molecules obtained from Open TG-GATES (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) (632 datasets), the hypothesis was tested that a short-term exposure, transcriptome-based liver biological effect POD (BEPOD) could estimate a longer-term exposure "systemic" apical endpoint POD. Apical endpoints considered were body weight, clinical observation, kidney weight and histopathology and liver weight and histopathology. A BMDExpress algorithm using Gene Ontology Biological Process gene sets was optimized to derive a liver BEPOD most predictive of a systemic apical POD. Liver BEPODs were stable from 3 hours to 29 days of exposure; the median fold difference of the 29 day BEPOD to BEPODs from earlier time points was approximately 1 (range of 0.7-1.1). Strong positive correlation (Pearson R = 0.86) and predictive accuracy (root mean square difference = 0.41) were observed between a concurrent (29 day) liver BEPOD and the systemic apical POD. Similar Pearson R and root mean square difference values were observed for comparisons between a 29 day systemic apical POD and liver BEPODs derived from 3 hours to 15 days of exposure. These data across 79 molecules suggest that a longer-term exposure study apical POD from liver and non-liver compartments can be estimated using a liver BEPOD derived from an acute or subacute exposure study.</p>
Data from: Complexity of frequency receptive fields predicts tonotopic variability across species
<p><span>Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these global tonotopic maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that local heterogeneity of frequency preferences is not unique to rodents. Using two-photon calcium imaging in layers 2/3, we found that local variance in frequency preferences is equivalent in ferrets and mice. Neurons with multipeaked frequency tuning are less spatially organized than those tuned to a single frequency in both species. Furthermore, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a sampling bias towards neurons with simple frequency tuning. These results help explain previous inconsistencies in cortical topography across species and recording techniques. </span></p>
Beef database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The beef database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets, and is further differentiated into All Beef, Breeders and Fatteners: BasicFarmType (18 rows), DetailedFarmType (75 rows), ClimateClass+BasicFarmType (270 rows), NUTS+BasicFarmType (2074 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Dairy database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</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.