We use the Enron email dataset to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges.

To build a graph G = (V, E), we use email addresses as nodes V. Every node v_i has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge e_ij between two nodes i and j if there is at least one email exchange between the corresponding addresses.

Column 'Count' in 'edges.csv' file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight.

The file 'nodes.csv' contains a dictionary that is a compressed representation of time-series. The format of the dictionary is Day->The Number Of Emails Sent By the Address During That Day. The total number of days is 1448.

'id-email.csv' is a file containing the actual email addresses.

DomainIQ is a comprehensive global Domain Name dataset for organizations that want to build cyber security, data cleaning and email marketing applications. The dataset consists of the DNS records for over 267 million domains, updated daily, representing more than 90% of all public domains in the world.

The data is enriched by over thirty unique data points, including identifying the mailbox provider for each domain and using AI based predictive analytics to identify elevated risk domains from both a cyber security and email sending reputation perspective.

DomainIQ from Datazag offers layered intelligence through a highly flexible API and as a dataset, available for both cloud and on-premises applications. Standard formats include CSV, JSON, Parquet, and DuckDB.

Custom options are available for any other file or database format. With daily updates and constant research from Datazag, organizations can develop their own market leading cyber security, data cleaning and email marketing applications supported by comprehensive and accurate data from Datazag. Data updates available on a daily, weekly and monthly basis. API data is updated on a daily basis.

The total number of user mailboxes in Umeå kommun and how many are active each day of the reporting period. A mailbox is considered active if the user sent or read any email.

Version 1.0 - This version is the final revised one.

This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (https://doi.org/10.5194/essd-13-4529-2021).

LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution. LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), Brazil (https://doi.org/10.5194/essd-12-2075-2020), Great Britain (https://doi.org/10.5194/essd-12-2459-2020) and Australia (https://doi.org/10.5194/essd-13-3847-2021), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.

We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.

Note: It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that NAs are indicated by the number -999 in the runoff time series.

Disclaimer: We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (https://www.gkd.bayern.de/en/rivers/discharge/tables) and Baden-Württemberg (https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer). Runoff data from the Czech Republic may not be used to set up operational warning systems (https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf).

License: This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.

Additional references: We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying paper.

Supplements: We have created additional files after publication (therefore non peer-reviewed): 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge. 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes. If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at

The Reminder extension for CKAN enhances data management by providing automated email notifications based on dataset expiry dates and update subscriptions. Designed to work with CKAN versions 2.2 and up, but tested on 2.5.2, this extension offers a straightforward mechanism for keeping users informed about dataset updates and expirations, promoting better data governance and engagement. The extension leverages a daily cron job to check expiry dates and trigger emails. Key Features: Data Expiry Notifications: Sends email notifications when datasets reach their specified expiry date. A daily cronjob process determines when to send these emails. Note that failure of the cronjob will prevent email delivery for that day. Dataset Update Subscriptions: Allows users to subscribe to specific datasets to receive notifications upon updates via a subscription form snippet that can be included in dataset templates. Unsubscribe Functionality: Includes an unsubscribe link in each notification email, enabling users to easily manage their subscriptions. Configuration Settings: Supports at least one recipient for reminder emails via configuration settings in the CKAN config file. Bootstrap Styling: Intended for use with Bootstrap 3+ for styling, but may still work with Bootstrap 2 with potential style inconsistencies. Technical Integration: The Reminder extension integrates into CKAN via plugins, necessitating the addition of reminder to the ckan.plugins setting in the CKAN configuration file. The extension requires database initialization using paster commands to support the subscription functionality. Setting up a daily cronjob is necessary for the automated sending of reminder and notification emails. Benefits & Impact: By implementing the Reminder extension, CKAN installations can improve data management and user engagement. Automated notifications ensure that stakeholders are aware of dataset expirations and updates, leading to better data governance, and more active user involvement in data ecosystems. This extension provides an easy-to-implement solution for managing data lifecycles and keeping users informed.

Summary

Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently lunched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, but the short temporal coverage of the data records has limited its applications in long-term studies. While Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This dataset contains 20 years (2002-present) global spatio-temporal consistent surface soil moisture, and will be updated in near real-time . The resolution is 36 km at daily scale, the projection is EASE-Grid2, and the data unit is m3 / m3.

This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017, 2021). This study transfers the merits of SMAP to AMSR-E/2 through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with AMSR-E/2 brightness temperature (TB) as input. Finally, long term soil moisture data are output. This dataset can reproduce the spatial and temporal distribution of SMAP soil moisture, with comparable accuracy as SMAP soil moisture product. This dataset also compares well with in situ SSM observations at 14 dense validation networks globally, with accuracy of 5% volumetric water content, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable though the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability.

No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using this dataset, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference Yao et al. (2021) when referring to the dataset in the text. Feel free to send us an email at yaopp@radi.ac.cn to let us know how you are using the data.

Data Update

We have successfully implemented near real-time data updates. We will update the data every 3 months here. For near real-time updates, please visit the following website https://doi.org/10.11888/Soil.tpdc.270960.

Contact

For questions, please email Panpan Yao at yaopp@radi.ac.cn and Hui Lu at luhui@tsinghua.edu.cn

Data Formatting and File Names

Formating: The soil moisture data is stored in netcdf format and Tiff format.

File name: the file name is“ yyyyddd.nc ” or “ yyyyddd.tif ”, where yyyy stands for year and ddd stands for Julian date. For example, 2003001.nc represents this document describe the global soil moisture distribution on the first day of 2003.

How to read data: The data is EASE-grid2 equal-area projection data (with varying latitude and longitude intervals), rather than usual equal-latitude-longitude data. (for more information about EASE-grid2 projection, please see https://nsidc.org/data/ease/ease_grid2.html. ) The NC file of data stores three variables: latitude matrix, longitude matrix and soil moisture matrix, which are latitude (406*1), longitude(964*1) and soil_moisture (406*964) respectively.

Reference way

Reference of data

Yao, P., Lu, H. (2020). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2022). National Tibetan Plateau / Third Pole Environment Data Center. https://doi.org/10.11888/Soil.tpdc.270960. https://cstr.cn/18406.11.Soil.tpdc.270960.

Article citation

1、Yao, P.P., Shi, J.C., Zhao, T.J., Lu, H. & Al-Yaari, A. (2017). Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing 9(1), 35.

2、Yao, P.P., Lu, H., Shi, J.C., Zhao, T.J., Yang K., Cosh, M.H., Gianotti, D.J.S., & Entekhabi, D. (2021). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019). Scientific Data, 8, 143 (2021). https://doi.org/10.1038/s41597-021-00925-8

SpaceKnow USA Supply Chain Premium Dataset gives you data (by locations and company) of US Supply Chain choke points in near-real-time as seen from satellite images. The uniqueness of this dataset lies in its granularity.

About dataset: We apply proprietary algorithms to SAR satellite imagery of key industrial, transportation, storage, and logistics locations to create daily indices of industry activity. Data was collected from more than 5,000 locations across the USA. Thanks to the use of SAR satellite technology, the quality of the SpaceKnow dataset is not influenced by weather fluctuations.

In total SpaceKnow USA Supply Chain dataset offers +50 specific indices with real-time insights. The premium dataset includes company-focused indices. This type of data can be used by investors to get insight on important KPIs such as revenue.

This dataset is:

Daily frequency History from Jan 2017 - present

Within one package we provide you with real-time insights into:

Port Container country-level indices(A container port or container terminal is a facility where cargo containers are transshipped between different transport vehicles, for onward transportation) Port Container indices for the major ports in US: Port of Los Angeles Port of Long Beach Port of New York & New Jersey Port of Savannah Port of Houston Port of Virginia Port of Oakland in California Port of South Carolina Port of Miami

Trucking Stop indices for the most important locations in the supply chain like: Iowa Nevada South Carolina Oregon North Carolina

Inland Containers index on a country-level

Logistics Center index on a country-level (Logistics centers are distribution hubs for finished goods that need to be transported to another location. We include logistics centers from companies like Amazon, Walmart, Fedex and others)

Logistics Center indices for states like: California New York Illinois Indiana South Carolina And many more…

Logistics Center indices for companies: Amazon Walmart Fedex

Research Reports Don't have the capacity to analyze the data? Let SpaceKnow's in-house economists do the heavy lifting so that you can focus on what's important. SpaceKnow writes research reports based on what the data from the US Supply Chain dataset package is showing. The document includes a detailed explanation of what is happening with supporting charts and tables. The reports are published on a monthly basis.

Delivery Mechanisms All of the delivery mechanisms detailed below are available as part of this package. Data is distributed only in the flat-table CSV format. Methods how to access the data: Dashboard - option that also offers data visualization within the webpage Automatic email delivery API access to our dataset Research reports - provided via email in PDF format

Client Support

Each client is assigned an account representative who will reach out periodically to make sure that the data packages are meeting your needs. Here are some other ways to contact SpaceKnow in case you have a specific question.

For delivery questions and issues: Please reach out to support@spaceknow.com

For data questions: Please reach out to product@spaceknow.com

For pricing/sales support: Please reach out to info@spaceknow.com or sales@spaceknow.com

This .zip folder contains the parameters of a superstatistical distribution of daily precipitation extremes for 20,561 sites in the world. The details about this distribution are available in the following paper: De Michele, C. and Avanzi, F. (2018): Superstatistical distribution of daily precipitation extremes: A worldwide assessment, Scientific Reports. These parameters were estimated using daily-precipitation data from the Global Historical Climatology Network (GHCN) daily, available at ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/The .zip folder includes one file for each of the 20,561 sites considered in the above paper. The name of each file refers to the station ID of the original site belonging to the Global Historical Climatology Network (GHCN) daily, to which the reader is referred for information regarding the location of each site. The structure of the file is standard as follows:- The first row reports the station ID and the value of the optimal threshold used in the superstatistical distribution (see the above paper for details, unit is mm10);- All the other rows report estimates of optimal parameters lambda, beta, and p0 for each year with at least 25 days of precipitation. Again, the reader is referred to the above paper for details about these parameters. Each row reports results for one year in the following order: (1) year on which parameters were estimated; (2) number of available precipitation observations; (3) estimated value of beta; (4) estimated value of lambda (unit is mm10); (5) estimated value of p0. Questions about the dataset should be sent to the following email address: carlo.demichele@polimi.it

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

PLEASE NOTE: This dataset, which includes all TLC Licensed Drivers who are in good standing and able to drive, is updated every day in the evening between 4-7pm. Please check the 'Last Update Date' field to make sure the list has updated successfully. 'Last Update Date' should show either today or yesterday's date, depending on the time of day. If the list is outdated, please download the most recent list from the link below. http://www1.nyc.gov/assets/tlc/downloads/datasets/tlc_medallion_drivers_active.csv This is a list of drivers with a current TLC Driver License, which authorizes drivers to operate NYC TLC licensed yellow and green taxicabs and for-hire vehicles (FHVs). This list is accurate as of the date and time shown in the Last Date Updated and Last Time Updated fields. Questions about the contents of this dataset can be sent by email to: licensinginquiries@tlc.nyc.gov.

Our France WhatsApp Broadcast Lead gives you access to verified customer details. It helps you send one message to many people instantly, making marketing faster and easier. With this dataset, your business can promote products, reach customers directly, and improve communication effectively. Every business needs growth and customer connections daily ? and this France broadcast lead makes it simple and efficient with the help of Lead to Database.

• Format: Excel / CSV (CRM-Ready)
• Fields: Name ? Phone ? WhatsApp ? Email ? Location
• Coverage: France (B2B + B2C)
• Best For: WhatsApp Broadcast ? Bulk SMS ? Direct Outreach

France Broadcast List | Verified & Easy Marketing

The France WhatsApp Broadcast List helps you find the right people quickly. You can share offers, updates, and promotions with thousands of verified contacts at once. It saves time, reduces marketing costs compared to traditional methods, and builds stronger customer trust. With this verified dataset, you can prepare a single marketing message and broadcast it nationwide.

• 95%+ Verified & Active WhatsApp Numbers
• Share Promotions, Offers & Updates Instantly
• Cost-Effective Compared to Traditional Marketing
• Nationwide Coverage of France Audience

France WhatsApp Marketing Database | Ready-to-Use

Our France WhatsApp Marketing Database is ready-to-use for multiple business purposes. From product launches, service reminders, and discount offers to event invites and important business notices, this tool gives flexibility and supports all marketing needs. Since the data is sourced from trusted providers, you can be sure your messages reach active users only.

• Fresh & Permission-Based Data
• Perfect for Product Launches ? Offers ? Events
• Affordable for Small & Large Businesses
• High ROI from Verified & Genuine Contacts

France WhatsApp Audience Campaign | Connect Personally

France WhatsApp Audience Campaigns provide a personal and efficient way to connect with customers. Messages feel direct and build stronger trust, which often converts into lasting customer relationships. This makes WhatsApp one of the most cost-effective marketing tools. Almost everyone checks WhatsApp frequently ? so your message will be seen faster than emails or ads.

• Personal & Direct Customer Engagement
• Wide Coverage with Low Marketing Cost
• Trusted Source: Lead to Database
• Ideal for Both B2B & B2C Campaigns

Our France WhatsApp Broadcast Data is a powerful tool to reach people directly where they are. It helps your business grow faster, build trust, and improve communication. Get this verified dataset today from Lead to Database and start running successful WhatsApp campaigns across the France.

Our China WhatsApp Broadcast Lead gives you access to verified customer details. It helps you send one message to many people instantly, making marketing faster and easier. With this dataset, your business can promote products, reach customers directly, and improve communication effectively. Every business needs growth and customer connections daily ? and this China broadcast lead makes it simple and efficient with the help of Lead to Database.

• Format: Excel / CSV (CRM-Ready)
• Fields: Name ? Phone ? WhatsApp ? Email ? Location
• Coverage: China (B2B + B2C)
• Best For: WhatsApp Broadcast ? Bulk SMS ? Direct Outreach

China Broadcast List | Verified & Easy Marketing

The China WhatsApp Broadcast List helps you find the right people quickly. You can share offers, updates, and promotions with thousands of verified contacts at once. It saves time, reduces marketing costs compared to traditional methods, and builds stronger customer trust. With this verified dataset, you can prepare a single marketing message and broadcast it nationwide.

• 95%+ Verified & Active WhatsApp Numbers
• Share Promotions, Offers & Updates Instantly
• Cost-Effective Compared to Traditional Marketing
• Nationwide Coverage of China Audience

China WhatsApp Marketing Database | Ready-to-Use

Our China WhatsApp Marketing Database is ready-to-use for multiple business purposes. From product launches, service reminders, and discount offers to event invites and important business notices, this tool gives flexibility and supports all marketing needs. Since the data is sourced from trusted providers, you can be sure your messages reach active users only.

• Fresh & Permission-Based Data
• Perfect for Product Launches ? Offers ? Events
• Affordable for Small & Large Businesses
• High ROI from Verified & Genuine Contacts

China WhatsApp Audience Campaign | Connect Personally

China WhatsApp Audience Campaigns provide a personal and efficient way to connect with customers. Messages feel direct and build stronger trust, which often converts into lasting customer relationships. This makes WhatsApp one of the most cost-effective marketing tools. Almost everyone checks WhatsApp frequently ? so your message will be seen faster than emails or ads.

• Personal & Direct Customer Engagement
• Wide Coverage with Low Marketing Cost
• Trusted Source: Lead to Database
• Ideal for Both B2B & B2C Campaigns

Our China WhatsApp Broadcast Data is a powerful tool to reach people directly where they are. It helps your business grow faster, build trust, and improve communication. Get this verified dataset today from Lead to Database and start running successful WhatsApp campaigns across the China.

The dataset is a collection of aggregated clinical parameters for the participants (such as clinical scores), parameters extracted from the utilized devices (such as average heart rate per day, average gait speed etc.), and coupled events about them (such as falls, loss of orientation etc.). It contains information which was collected during the clinical evaluation of the older people from medical experts.This information represents the clinical status of the older person across different domains, e.g. physical, psychological, cognitive etc.

The dataset contains several medical features which are used by clinicians to assess the overall state of the older people.

The purpose of the Virtual Patient Model is to assess the overall state of the older people based on their medical parameters, and to find associations between these parameters and frailty status.

A list of the recorded clinical parameters and their description is shown below:

- part_id: The user ID, which should be a 4-digit number

- q_date: The recording timestamp, which follows the “YYYY-MM-DDTHH:mm:ss.fffZ” format (eg. 14 September 2017 12:23:34.567, is formatted as 2019-09-14T12:23:34.567Z)

- clinical_visit: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to

- fried: Ordinal categorization of frailty level according to Fried operational definition of frailty

- hospitalization_one_year: Number of nonscheduled hospitalizations in the last year

- hospitalization_three_years: Number of nonscheduled hospitalizations in the last three years

- ortho_hypotension: Presence of orthostatic hypotension

- vision: Visual difficulty (qualitative ordinal evaluation)

- audition: Hearing difficulty (qualitative ordinal evaluation)

- weight_loss: Unintentional weight loss >4.5 kg in the past year (categorical answer)

- exhaustion_score: Self-reported exhaustion (categorical answer)

- raise_chair_time: Time in seconds to perform a lower limb strength clinical test

- balance_single: Single foot station (Balance) (categorical answer)

- gait_get_up: Time in seconds to perform the 3meters’ Timed Get Up And Go Test

- gait_speed_4m: Speed for 4 meters’ straight walk

- gait_optional_binary: Gait optional evaluation (qualitative evaluation by the investigator)

- gait_speed_slower: Slowed walking speed (categorical answer)

- grip_strength_abnormal: Grip strength outside the norms (categorical answer)

- low_physical_activity: Low physical activity (categorical answer)

- falls_one_year: Number of falls in the last year

- fractures_three_years: Number of fractures during the last 3 years

- fried_clinician: Fried’s categorization according to clinician’s estimation (when missing data for answering the Fried’s operational frailty definition questionnaire)

- bmi_score: Body Mass Index (in Kg/m²)

- bmi_body_fat: Body Fat (%)

- waist: Waist circumference (in cm)

- lean_body_mass: Lean Body Mass (%)

- screening_score: Mini Nutritional Assessment (MNA) screening score

- cognitive_total_score: Montreal Cognitive Assessment (MoCA) test score

- memory_complain: Memory complain (categorical answer)

- mmse_total_score: Folstein Mini-Mental State Exam score

- sleep: Reported sleeping problems (qualitative ordinal evaluation)

- depression_total_score: 15-item Geriatric Depression Scale (GDS-15)

- anxiety_perception: Anxiety auto-evaluation (visual analogue scale 0-10)

- living_alone: Living Conditions (categorical answer)

- leisure_out: Leisure activities (number of leisure activities per week)

- leisure_club: Membership of a club (categorical answer)

- social_visits: Number of visits and social interactions per week

- social_calls: Number of telephone calls exchanged per week

- social_phone: Approximate time spent on phone per week

- social_skype: Approximate time spent on videoconference per week

- social_text: Number of written messages (SMS and emails) sent by the participant per week

- house_suitable_participant: Subjective suitability of the housing environment according to participant’s evaluation (categorical answer)

- house_suitable_professional: Subjective suitability of the housing environment according to investigator’s evaluation (categorical answer)

- stairs_number: Number of steps to access house (without possibility to use elevator)

- life_quality: Quality of life self-rating (visual analogue scale 0-10)

- health_rate: Self-rated health status (qualitative ordinal evaluation)

- health_rate_comparison: Self-assessed change since last year (qualitative ordinal evaluation)

- pain_perception: Self-rated pain (visual analogue scale 0-10)

- activity_regular: Regular physical activity (ordinal answer)

- smoking: Smoking (categorical answer)

- alcohol_units: Alcohol Use (average alcohol units consumption per week)

- katz_index: Katz Index of ADL score

- iadl_grade: Instrumental Activities of Daily Living score

- comorbidities_count: Number of comorbidities

- comorbidities_significant_count: Number of comorbidities which affect significantly the person’s functional status

- medication_count: Number of active substances taken on a regular basis

Features reports based on forecast severe weather conditions. such as high winds, blizzard conditions, possible severe thunderstorms, hurricane conditions, and heavy snow. Advisories are available for all 85,000 worldwide locations in CustomWeather’s global weather database. The severe weather data advisories are updated four times per day.

The product returns severe weather advisories based on the forecast for the next six or 24 hours or five days broken down into segments of the day (Morning, Afternoon, Evening, Overnight). Global Weather Data.

Custom alerts can be generated for any specific weather criteria, either in the past based on climate data or in the future based on weather forecasts. Weather alerts can also be generated that incorporate both past and future weather data. CustomWeather's custom alerts can be sent out via email to specific user groups, via FTP, or via SMS using carrier email-to-SMS transmission.

This severe weather data represents a part of CustomWeather's trove of historical, real-time, and forecast data sets covering the entire life cycle of weather - past, present, and future.

The Global Severe Weather Advisories includes information included in the following data categories: Environmental Data, Event Data, Geographic Data, Global Weather Data, Insurance Data, Lightning Data, Natural Disasters Data, News Data, Places Data, Precipitation Data, Rainfall Data, Severe Weather Data, Storm Data, Temperature Data, and Wind Data.

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the CustomWeather 100 or CW100. The CW100 Model is based on physics, not statistics or airport observations. As a result, it can achieve significantly better accuracy than statistical models, especially for non-airport locations. While other forecast models are designed to forecast the entire atmosphere, the CW100 greatly reduces computational requirements by focusing entirely on conditions near the ground. This reduction of computations allows the model to resolve additional physical processes near the ground that are not resolved by other models. It also allows the CW100 to operate at a much higher resolution, typically 100x finer than standard models and other gridded forecasts.

This is a listing and mapping of the Remote Working Hubs available to rent on a daily or weekly basis from the the Enterprise Centres within Fingal County Council. Each Enterprise Center needs to be contacted individually to arrrange bookings as each is it own entity and due to demand some may not be available. B.E.A. T. Enterprise Center - contact via email or phone paula@innovatefingal.ie Drinan Enterprise Centre - contact via email or phone marie@innovatefingal.ieBase Enterprise Centre - contact via email or phone Reception@innovatefingal.ie