Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly. Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents, by date of death. Description The MD COVID-19 - Total Confirmed Deaths by Date of Death data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by date of death. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Total Probable Deaths by Date of Death data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

NOTE: To review the latest plan, make sure to filter the "Report Year" column to the latest year. This is a list of civic engagement activities reported by city agencies for the purpose of providing context, spreading awareness, and increasing the use of public data. Details include activity descriptions, date of completion and links to further information on the reported activity. For the historical 2018-2022 data please refer to https://data.cityofnewyork.us/d/keb3-v9xt/

The Integrated Multi-Mission Ocean Altimeter Sea Surface Height (SSH) Version 5.2 dataset provides level 2 along track sea surface height anomalies (SSHA) from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A missions geo-referenced to a mean reference orbit. It is produced by NASA Sea Surface Height (NASA-SSH) project investigators at Goddard Space Flight Center and Jet Propulsion Laboratory with support from NASA’s Physical Oceanography program, and was developed originally as an Earth System Data Record (ESDR) under the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, which supported forward processing and incremental refinements through version 5.1 (released in April 2022).Geophysical Data Records (GDRs) from each altimetry mission were interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. The entire multi-mission data record covers the period from September 1992 to present; it is extended to include new observations approximately once each quarter. The previous release (version 5.1) integrated Jason-3 data and applied revised internal tides and pole tide across missions (GDR_F standard). The current release (version 5.2) includes the following revisions: a) GSFC std2006_cs21 orbit for all missions, b) GOT5.1 ocean tide model, c) TOPEX/Poseidon GDR_F data, d) Sentinel-6 LR version F08 data, e) Jason-3 re-calibrated radiometer wet troposphere correction. More information about the data content and derivation can be found in the v5.2 User’s Handbook (https://doi.org/10.5067/ALTUG-TJ152).Please note that this collection is the same data as https://doi.org/10.5067/ALTCY-TJA52 but with all cycles included in one netCDF file.

Dataset

This dataset was created by Ayusman Samasi

Released under Apache 2.0

Contents

Whole Foods Products Data

Products scraped from whole foods

About this dataset

This data is scraped from Whole Foods Featured On Sale Web Page. The features regular, sale, and prime depict the possible 3 different product prices depending on the customer's store membership status.

The data provides insight into which products are on sale at Whole Foods and how much of a discount each product is offered. This dataset could be used to improve customer experience by providing them with information on which products are on sale and how much they are discounted. Additionally, this dataset could be used to inform marketing decisions or to develop targeted marketing campaigns

How to use the dataset

How to use this dataset

This data is scraped from Whole Foods Featured On Sale Web Page. The features regular, sale, and prime depict the possible 3 different product prices depending on the customer's store membership status. The 'aisle' column indicates which aisle the product can be found in and the 'category' column indicates what type of product it is.

The 'rules' file contains data on items that are on sale at Whole Foods. It includes the item, the sale price, and the discount percentage

Research Ideas

• Find products that are frequently purchased together.
• Analyze products that are on sale at a discount.
• Study discounts and and items frequently purchased together

Acknowledgements

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Uncleaned_WholeFoods_Sale_Data.csv | Column name | Description | |:--------------|:--------------| | **** | | | company | | | product | | | regular | | | sale | | | prime | |

File: ailes.csv | Column name | Description | |:---------------|:--------------| | **** | | | instacart | | | wholefoods | |

File: apriori.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------------------| | **** | | | itemA | The first item in the transaction. (String) | | itemB | The second item in the transaction. (String) | | freqAB | The number of times itemA and itemB appear together in a transaction. (Numeric) | | supportAB | The support of itemA and itemB appearing together in a transaction. (Numeric) | | freqB | The number of times itemB appears in a transaction. (Numeric) | | supportB | The support of itemB appearing in a transaction. (Numeric) | | lift | The lift of itemA and itemB appearing together in a transaction. (Numeric) |

File: categories.csv | Column name | Description | |:--------------|:--------------------------------------| | **** | | | category | The category of the product. (String) |

File: items.csv | Column name | Description | |:--------------|:-------------------------------------| | **** | | | items | The items that are on sale. (String) |

File: orders_parsed.csv | Column name | Description | |:-------------------|:----------------------------------------------| | **** | | | product | | | aisle | The aisle the product is located in. (String) | | parsed_product | The product name. (String) |

File: orders_parsed2.csv | Column name | Description | |:-------------------|:----------------------------------------------| | product | | | aisle | The aisle the product is located in. (String) | | parsed_product | The product name. (String) |

File: orders_parsed3.csv | Column name | Description ...

Descriptive statistics for the participants with complete data on the main variables in the study.

This dataset consists of ground-based Satellite Laser Ranging observation data (full-rate, monthly files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The monthly SLR full-rate observation files contain data received in the month from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Full-rate_data.html.

This is a complete dataset of IPL from 2008 till 2022 which contains 2 files i.e.., ball to ball dataset and also matches Dataset

This dataset tracks the updates made on the dataset "Pre-2012 Home Health Agencies & Hospice Annual Utilization Report - Complete Data Set" as a repository for previous versions of the data and metadata.

The dataset contains complete current RÚIAN data, i.e. complete descriptive data and spatial limitations of territorial elements and territorial registration units for the selected municipality (1 set) or for the whole state (3 sets). For each element, all available descriptive attributes and a definition point (if any) are listed. The following elements are included in the files for individual municipalities (mark OB_UKSH): the municipality, parts of the municipality, municipal districts/municipal districts (MOMC) for territorially disaggregated statutory towns, for Prague city districts of Prague (MOP) and administrative districts of Prague (SOP), cadastral territory, basic settlement units (ZSJ), streets (including the closing lines of streets), plots (including polygon), building buildings (including polygon) and address places, for the above mentioned higher territorial elements in addition contains their original boundaries. The ST_UKSG file for the whole state contains the following elements: the state, cohesion regions, higher local authorities (VÚSC), municipalities with extended competence (ORP), municipalities with delegated municipal authority (POU), regions (from 1960), districts, municipalities, parts of the municipality, MOMC, ILO, CLO, cadastral territory and ZSJ and for all elements contain their generalised borders (if they do not exist, then the original borders). The ST_UKSH file for the whole state contains the following elements: the state, cohesion regions, VÚSC, ODA, POU, regions (old 1960) and districts, for all elements, also contain their original borders. The ST_UKSO file for all municipalities and MOMC contains images of flags and characters. The data set is provided as open data (CC-BY 4.0 license). The data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estate). Data are generated once a month (on the first day of each month with data valid on the last day of the previous month) in the exchange format RÚIAN (VFR), which is based on XML language and conforms to GML 3.2.1 (according to ISO 19136:2007). For download, each file is compressed as ZIP. More in Act No. 111/2009 Coll., on basic registers, in Decree No 359/2011 Coll., on the basic register of territorial identification, addresses and real estate.

Cuba CU: Renewable Energy Consumption: % of Total Final Energy Consumption data was reported at 20.900 % in 2021. This records a decrease from the previous number of 24.500 % for 2020. Cuba CU: Renewable Energy Consumption: % of Total Final Energy Consumption data is updated yearly, averaging 25.400 % from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 51.300 % in 1992 and a record low of 15.600 % in 2010. Cuba CU: Renewable Energy Consumption: % of Total Final Energy Consumption data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cuba – Table CU.World Bank.WDI: Environmental: Energy Production and Consumption. Renewable energy consumption is the share of renewables energy in total final energy consumption.;IEA, IRENA, UNSD, World Bank, WHO. 2023. Tracking SDG 7: The Energy Progress Report. World Bank, Washington DC. © World Bank. License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO).;Weighted average;

Total-Yield-That-Is-Dividend-Plus-Net-Buyback-Yield Time Series for Automatic Data Processing Inc. Automatic Data Processing, Inc. provides cloud-based human capital management (HCM) solutions worldwide. It operates in two segments, Employer Services and Professional Employer Organization (PEO). The Employer Services segment offers strategic, cloud-based platforms, and human resources (HR) outsourcing solutions. This segment's offerings include RUN Powered by ADP, a software platform for small business payroll, HR, and compliance; ADP Workforce Now, a HCM solution used across mid-sized and large businesses to manage employees; and ADP Lyric HCM, a solution for HR management, payroll, workforce management, talent, and data analytics. The PEO Services segment provides HR and employment administration outsourcing solutions under ADP TotalSource name to businesses through a co-employment model. The segment also provides guidance, user-friendly technology, comprehensive employee benefits, and a risk management, safety, and workers' compensation program. The company was founded in 1949 and is headquartered in Roseland, New Jersey.

Home Health Agencies (HHA) provide at home skilled nursing, personal care and therapeutic services. Hospices provide palliative care and alleviate the physical, emotional, social and spiritual discomforts of an individual who is experiencing the last phases of life due to the existence of a terminal disease. In addition, hospices provide supportive care for the primary care giver and the family of the hospice patient. Home health agencies and hospices submit an annual utilization report to the Office at the end of each calendar year. The report includes information on services capacity, visits, utilization, patient characteristics, and capital/equipment expenditures, and gross revenues. The documentation, including report forms, is available for each reporting year.

Context

Dogecoin is an open source peer-to-peer digital currency, favored by Shiba Inus worldwide. It is qualitatively more fun while being technically nearly identical to its close relative Bitcoin. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system.

Content

You can access the data from BigQuery in your notebook with bigquery-public-data.crypto_dogecoin dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_dogecoin.[TABLENAME].

Acknowledgements

This dataset wouldn't be possible without the help of BigQuery and all of their contributions to public data.

NOTE: This layer is deprecated (last updated 3/14/2022). This was formerly a daily update.

Summary The cumulative number of COVID-19 positive Maryland residents who have been hospitalized.

Description The MD COVID-19 - Total Hospitalizations data layer is a collection of the statewide cumulative total of individuals who tested positive for COVID-19 that have been reported each day by each local health department as having been hospitalized. As published to coronavirus.maryland.gov, this is the "Ever Hospitalized" number. "Ever Hospitalized" refers to the cumulative number of individuals who were admitted to the hospital at some point during their COVID-19 illness.

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Mountain glaciers are closely coupled to climate processes, ecosystems, and regional water resources. To enhance physical understanding of these connections, the USGS maintains a collection of glacier mass balance and climate data across the western United States and Alaska. In some cases, records of glacier mass balance extend back to the mid-1940s. These data have been incorporated from various sources, primarily original USGS studies, but also including work from the University of Alaska, and the Juneau Icefield Research Program (JIRP). The core of this collection is composed of mass balance data from the USGS Benchmark Glaciers. These five glaciers are Lemon Creek Glacier, AK (1953 -Present), South Cascade Glacier, WA (1958 - Present), Gulkana and Wolverine glaciers, AK (1966 - Present), and Sperry Glacier, MT (2005 - Present). Datasets from each benchmark glacier are composed of, at a minimum, point mass balances, glacier hypsometry, daily temperature and precipitation, geode ...

ParlSpeech V2 contains complete full-text vectors of more than 6.3 million parliamentary speeches in the key legislative chambers of Austria, the Czech Republic, Germany, Denmark, the Netherlands, New Zealand, Spain, Sweden, and the United Kingdom, covering periods between 21 and 32 years. Meta-data include information on date, speaker, party, and partially agenda item under which a speech was held. The accompanying release note provides a more detailed guide to the data.

The data set contains complete current RÚIAN data, i.e. complete descriptive data and spatial delimitation of territorial elements and territorial registration units for the selected municipality (1 file) or for the whole state (3 sets). For each element, all available descriptive attributes and definition point (if any) are given. The following elements are included in the files for each municipality (the OB_UKSH designation): the municipality, parts of the municipality, municipal districts/urban districts (MOMC) for the territorially divided statutory cities, for Prague City Districts of Prague (ILO) and the administrative districts of Prague (SOP), cadastral territory, basic settlement units (ZSJ), streets (including street definition lines), parcels (including polygon), building buildings (including polygon) and address points, for the above mentioned higher territorial elements also contain their original boundaries. The state-wide ST_UKSG file contains the following elements: the state, cohesion regions, higher territorial self-governing units (VÚSC), municipalities with extended competence (ORP), municipalities with a mandated municipal authority (POU), regions (from 1960), districts, municipalities, parts of the municipality, MOMC, ILO, SOP, cadastral territory and ISC, and for all elements contain their generalised boundaries (if they do not exist, then original boundaries). The ST_UKSH file for the entire country contains the following elements: the state, cohesion regions, VÚSC, ODA, POU, regions (old from 1960) and districts, for all elements, also contains their original boundaries. The ST_UKSO file for all municipalities and MOMC contains images of flags and characters. The data set is provided as open data (CC-BY 4.0 license). The data is based on RÚIAN (Registry of Territorial Identification, Addresses and Real Estate). Data are generated once a month (on the first day of each month with data valid on the last day of the previous month) in an exchange format RÚIAN (VFR), which is based on XML language and conforms to GML standard 3.2.1 (according to ISO 19136:2007). For download, each file is compressed as a ZIP. More in Act No. 111/2009 Coll., on basic registers, in Decree No. 359/2011 Coll., on the basic register of territorial identification, addresses and real estate.

This dataset contains historical data on concentrations of total and dissolved organic carbon in Massachusetts streams from the U.S. Geological Survey National Water Information System (NWIS) database. The data were compiled from NWIS using site and sample selection criteria to retrieve all publicly available data from surface-water samples that contained analysis of both total and dissolved organic carbon. The data set was screened, as much as possible from the site and sample description information in NWIS, to retain only routine environmental samples from stream sites. The final data set consists of 223 samples from 52 sites and were collected from 1978 to 2007.