This dataset presents detailed building operation data from the three blocks (A, B and C) of the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX. The aim of PHOENIX is to improve buildings efficiency, and therefore we included information of:
(i) consumption data, aggregated by block in kWh; (ii) HVAC (Heating, Ventilation and Air Conditioning) data with several features, such as state (ON=1, OFF=0), operation mode (None=0, Heating=1, Cooling=2), setpoint and device type; (iii) indoor temperature per room; (iv) weather data, including temperature, humidity, radiation, dew point, wind direction and precipitation; (v) carbon dioxide and presence data for few rooms; (vi) relationships between HVAC, temperature, carbon dioxide and presence sensors identifiers with their respective rooms and blocks. Weather data was acquired from the IMIDA (Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario).

Dataset

This dataset was created by hardly_human

Released under U.S. Government Works

Contents

Dataset

This dataset was created by Vinay Shaw

Contents

PANDA is the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world scenes with both wide field-of-view (~1 square kilometer area) and high-resolution details (~gigapixel-level/frame). The scenes may contain 4k head counts with over 100x scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7k trajectories, 2.2k groups and 2.9k interactions.

The publication "Scientific Data Analysis and Visualisation with Python" delves into various facets of Python programming, with a special focus on data analysis and visualisation. Let us deconstruct the main sections: Examining operators and expressions: The text explores arithmetic, comparison, logic, bitwise, assignment and membership operators. These operators serve as fundamental components in the construction of any Python script. Illustrative real-world scenarios show the practical applications of these operators. For example, arithmetic operators are essential for performing mathematical calculations, while comparison operators facilitate decision-making processes. Discussion of data structures and control flow: The book discusses procedures for input, handling strings, working with lists, dictionaries, loops, and conditional expressions. Scientists and software developers can learn how to manipulate data structures efficiently. In particular, lists and dictionaries play a crucial role in organising and retrieving data. Insight into functions and modularisation: Functions are central to Python programming. The publication offers valuable perspectives on the creation and use of functions. The process of modularisation increases the reusability and maintainability of code. By breaking down complex tasks into smaller functions, developers can improve the understandability of their code. Exploring data with Pandas: The book presents a detailed examination of Pandas, a robust library. Readers will gain skills in loading, manipulating, and analysing data frames. Explain data presentation and visualisation: Effective visualisation is critical to understanding data. The publication introduces matplotlib and other plotting libraries. Scientific researchers and analysts can create powerful visual representations to effectively communicate insights. In summary, this publication serves as a valuable resource for individuals at various levels of Python proficiency, including beginners and experienced users. Whether you are a scientist navigating through data or a developer honing your skills, the comprehensive content in this book will guide you towards mastering Python data analysis and visualisation. The training materials are provided for international learners. However, the following lectures on Python are available on YouTube for both international and Bangladeshi learners. For international learners: https://youtube.com/playlist?list=PL4T8G4Q9_JQ9ci8DAhpizHGQ7IsCZFsKu For Bangladeshi learners: https://youtube.com/playlist?list=PL4T8G4Q9_JQ_byYGwq3FyGhDOFRNdHRL8 My profile: https://researchsociety20.org/founder-and-director/

This resource serves as a template for creating a curve number grid raster file which could be used to create corresponding maps or for further utilization, soil data and reclassified land-use raster files are created along the process, user has to provided or connect to a set of shape-files including boundary of watershed, soil data and land-use containing this watershed, land-use reclassification and curve number look up table. Script contained in this resource mainly uses PyQGIS through Jupyter Notebook for majority of the processing with a touch of Pandas for data manipulation. Detailed description of procedure are commented in the script.

Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for SEAnorm, a time normalized superposed epoch analysis package in python.

Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format.

The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence.

The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.

These data are derived from analyses based on PANDA results and are consistent with those presented in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".

To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:

"Analysis/simulations/paired_scenario.zip"

"Analysis/simulations/unpaired_scenario.zip"

"Analysis/simulations/merfish.zip"

"Analysis/simulations/reference_choice.zip"

"Analysis/simulations/parameter_sensitivity.zip"

"Analysis/simulations/time_memory.zip"

"Analysis/applications/melanoma.zip"

"Analysis/applications/mouse_brain.zip"

"Analysis/applications/human_heart.zip"

The 50 most abundant LC-HRMS signals selected from the OPLS loading plot of of YY’s 21 samples. The LC-MS features are listed in the order of their LC retention times (Rt). Italicised names indicate non-proton adducts and complex ions identified by MZMine 2.10, and confirmed by manually checking the raw LC-HRMS data. The metabolite annotations are based on the Metabolomics Standards Initiative (MSI) identification levels. Level 1, retention times matched with authentic standards (labelled as ST); level 2, identified by MS/MS (labelled as MS); level 3, accurate mass; and level 4, unidentified. The metabolites identified at levels 1 and 2 are also labelled with the compound identifiers (CID) codes from the PubChem database.Compounds in giant panda milk tentatively correlated with cub growth rate.