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  • Reports of major limb defects after prenatal cannabis exposure (PCE) in animals and of human populations in Hawaii, Europe and Australia raise the question of whether the increasing use of cannabis in USA might be spatiotemporally associated with limb reduction rates (LRR) across USA. Geotemporospatial analysis conducted in R. LRR was significantly associated with cannabis use and THC potency and demonstrated prominent cannabis-use quintile effects. In final lagged geospatial models interactive terms including cannabinoids were highly significant and robust to adjustment. States in which cannabis was not legalized had a lower LRR (4.28 v 5.01 /10,000 live births, relative risk reduction = -0.15, (95%C.I. -0.25, -0.02), P=0.021). 37-63% of cases are estimated to not be born alive; their inclusion strengthened these associations. Causal inference studies using inverse probabilty-weighted robust regression and e-values supported causal epidemiological pathways. Findings apply to several cannabinoids, are consistent with pathophysiological and causal mechanisms, are exacerbated by cannabis legalization and demonstrate dose-related intergenerational sequaelae.
    Data Types:
    • Other
    • Software/Code
    • Geospatial Data
    • Tabular Data
    • Dataset
  • The geographic data sources were the digital elevation model (DEM) with the resolution of 10 m, land use with the resolution of 10 m, catchment boundary and drainage network, which were gathered for subcatchment subdivision for urban stormwater model development.
    Data Types:
    • Software/Code
    • Geospatial Data
    • Image
    • Dataset
    • Document
    • File Set
  • Dataset general description: • This dataset reports 4195 recurrent neural network models, their settings, and their generated prediction csv files, graphs, and metadata files, for predicting COVID-19's daily infections in Brazil by training on limited raw data (30 time-steps and 40 time-steps alternatives). The used code is developed by the author and located in the following online data repository link: http://dx.doi.org/10.17632/yp4d95pk7n.1 Dataset content: • Models, Graphs, and csv predictions files: 1. Deterministic mode (DM): includes 1194 generated models files (30 time-steps), and their generated 2835 graphs and 2835 predictions files. Similarly, this mode includes 1976 generated model files (40 time-steps), and their generated 7301 graphs and 7301 predictions files. 2. Non-deterministic mode (NDM): includes 20 generated model files (30 time-steps), and their generated 53 graphs and 53 predictions files. 3. Technical validation mode (TVM): includes 1001 generated model files (30 time-steps), and their generated 3619 graphs and 3619 predictions files for 358 models, which are a sample of 1001 models. Also, 1 model in control group for India. 4. 1 graph and 1 prediction files for each of DM and NDM, reporting evaluation till 2020-07-11. • Settings and metadata for the above 3 categories: 1. Used settings in json files for reproducibility. 2. Metadata about training and prediction setup and accuracy in csv files. Raw data source that was used to train the models: • The used raw data for training the models is from: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University): https://github.com/CSSEGISandData/COVID-19 • The models were trained on these versions of the raw data: 1. Link till 2020-06-29 (accessed 2020-07-08): https://github.com/CSSEGISandData/COVID-19/raw/78d91b2dbc2a26eb2b2101fa499c6798aa22fca8/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv 2. Link till 2020-06-13 (accessed 2020-07-08): https://github.com/CSSEGISandData/COVID-19/raw/02ea750a263f6d8b8945fdd3253b35d3fd9b1bee/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv License: This prediction Dataset is licensed under CC BY NC 3.0. Notice and disclaimer: 1- This prediction Dataset is for scientific and research purposes only. 2- The generation of this Dataset complies with the terms of use of the publicly available raw data from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University: https://github.com/CSSEGISandData/COVID-19 and therefore, the author of the prediction Dataset disclaims any and all responsibility and warranties regarding the contents of used raw data, including but not limited to: the correctness, completeness, and any issues linked to third-party rights.
    Data Types:
    • Other
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
  • The present dataset includes data and figures related to the submitted paper " Modeling and pseudo-inversion of transient multicomponent electromagnetic logging while drilling measurements " currently under review in Journal of Petroleum Science & Engineering.
    Data Types:
    • Software/Code
    • Image
    • Dataset
  • The National Wet Waste Inventory is a point spatial dataset containing >56,000 modeled sources of municipal wastewater sludge; confined animal manure; industrial, institutional, and commercial (IIC) food waste; and fats, oils, and greases (FOG). These wastes are considered priority organic feedstocks for waste-to-energy conversion. While the dataset is spatially explicit to enable geospatial analysis, the point locations and the associated waste magnitudes are considered representative and are intended to be used for national scale resource assessment modeling and may not reflect current on the ground conditions at a given location.
    Data Types:
    • Software/Code
    • Geospatial Data
    • Tabular Data
    • Dataset
    • Text
    • File Set
  • This is the analysis conducted and dataset for the study entitled "Factors associated with university and hospital mistreatment in medical students from seven Latin American countries".
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
  • The present dataset includes data and figures related to the submitted paper " Detection of formation boundary using transient multicomponent electromagnetic logging while drilling method " currently under review in Journal of Petroleum Science and Engineering.
    Data Types:
    • Software/Code
    • Image
    • Dataset
  • Bio-layer interferometry (BLI) traces (response vs. time), chromatin immunoprecipitation with qPCR (ChIP-qPCR) data, Bacterial one-hybrid (B1H-seq) fitness data, Bacterial one-hybrid plate-streak assays data (uncropped images), Curves+ data for DNA grooves widths/twist angle/roll angle, PDB validation reports.
    Data Types:
    • Other
    • Image
    • Sequencing Data
    • Tabular Data
    • Dataset
    • Document
  • This dataset is supplementary data for: Waldron, A., Pecci, F., Stoianov, I. (2020). Regularization of an Inverse Problem for Parameter Estimation in Water Distribution Networks. Journal of Water Resources and Planning Management, 146(9):04020076 (https://doi.org/10.1061/(ASCE)WR.1943-5452.0001273). The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence. Any use of this dataset must credit the authors by citing the above paper. BWFLnet is an operational network in Bristol, UK, operated by Bristol Water in collaboration with the InfraSense Labs at Imperial College London and Cla-Val Ltd. The data provided is a the product of a long term research partnership between Bristol Water, Infrasense Labs at Imperial College London and Cla-Val on the design and control of dynamically adaptive networks. We acknowledge the financial support of EPSRC (EP/P004229/1, Dynamically Adaptive and Resilient Water Supply Networks for a Sustainable Future). All data provided is recorded hydraulic data with locations and names anonymised. The authors hope that the publication of this dataset will facilitate the reproducibility of research in hydraulic model calibration as well as broader research in the water distribution sector.
    Data Types:
    • Geospatial Data
    • Tabular Data
    • Dataset
    • Document
    • File Set
  • The experimental tests data were collected on the counter burner. The fuel (Methane, Propane, and LPG) and air flow rates were measured at the inlet of the test section before mixing chamber. Images for a flame disc and the double disc with high contrast were recorded at changing the nozzle diameters for the burners and fuel type for wide ranges of equivalence ratios within range of 0.46 < φ < 1.57. Image processing algorithm was developed to extract information from the recorded images
    Data Types:
    • Tabular Data
    • Dataset
    • Document
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